--------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jmorales\Dropbox\adamowicz\4_Draft\CPS_FINAL_submission\Replication_code\cps_final.log
  log type:  text
 opened on:  15 Nov 2021, 16:14:53

. 
. do "1_twitter_main.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All tables and graphs using main Twitter data
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. 
. * Path 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}tweets_poland.dta", clear

. 
. ***Basic data cleaning and generate basic controls****
. sort screen_name

. merge screen_name using "${PathData}mayors.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)
variable screen_name does not uniquely identify observations in the master data
(note: variable screen_name was str15, now str16 to accommodate using data's values)

. drop if mayor==1
(7,541 observations deleted)

. drop mayor

. drop _merge

. 
. gen date = substr(created_at,1,10)
(2 missing values generated)

. gen month = substr(date,6,2)
(2 missing values generated)

. gen year = substr(date,1,4)
(2 missing values generated)

. gen day = substr(date,9,2)
(2 missing values generated)

. gen hour = substr(created_at,12,2)
(2 missing values generated)

. destring(month), replace
month: all characters numeric; replaced as byte
(2 missing values generated)

. destring(year), replace
year: all characters numeric; replaced as int
(2 missing values generated)

. destring(day), replace
day: all characters numeric; replaced as byte
(2 missing values generated)

. destring(hour), replace
hour: all characters numeric; replaced as byte
(2 missing values generated)

. gen time = (year-2012)*12 + month
(2 missing values generated)

. gen timeD = (year-2012)*365 + (month==2)*31 + (month==3)*(31+28) + (month==4)*(31*2 + 28) + (month==5)*(31*2 + 28 +30) + (month==6)*
> (31*3 + 28 +30) + (month==7)*(31*3 + 28 + 30*2) + (month==8)*(31*4 + 28 + 30*2) + (month==9)*(31*5 + 28 + 30*2) + (month==10)*(31*5 
> + 28 + 30*3) + (month==11)*(31*6 + 28 + 30*3) + (month==12)*(31*6 + 28 + 30*4) + day + 1*((month>2 & year==2012)|(year>2012))
(2 missing values generated)

. gen week = round(timeD/7)
(2 missing values generated)

. gen date2 = mdy(month, day, year)
(2 missing values generated)

. 
. gen text_l = lower(text)
(6 missing values generated)

. replace text = text_l
(321,402 real changes made)

. drop text_l

. replace text = subinstr(text, "á", "a",.) 
(153 real changes made)

. replace text = subinstr(text, "é", "e",.) 
(201 real changes made)

. replace text = subinstr(text, "í", "i",.) 
(38 real changes made)

. replace text = subinstr(text, "ó", "o",.) 
(121,239 real changes made)

. replace text = subinstr(text, "ú", "u",.)
(2 real changes made)

. replace text = subinstr(text, "ñ", "nh",.)
(17 real changes made)

. replace text = subinstr(text, ",", " ",.)
(136,152 real changes made)

. replace text = subinstr(text, `"""',  "", .)
(16,551 real changes made)

. replace text = subinstr(text, ".", " ",.) 
(259,160 real changes made)

. replace text = subinstr(text, "-", " ",.)
(41,816 real changes made)

. replace text = subinstr(text, "!", " ",.) 
(38,944 real changes made)

. replace text = subinstr(text, "/", " ",.)
(151,751 real changes made)

. replace text = subinstr(text, "…", " ",.)
(0 real changes made)

. replace text = subinstr(text, ":", " ",.)
(292,829 real changes made)

. replace text = subinstr(text, ";", " ",.)
(3,847 real changes made)

. * replace text = subinstr(text, "#", "",.)
. * replace text = subinstr(text, "@", "",.)
. forvalues i = 1(1)7 {
  2.         replace text = subinstr(text, "  ", " ",.)
  3. }
(309,299 real changes made)
(197,049 real changes made)
(3,922 real changes made)
(267 real changes made)
(125 real changes made)
(37 real changes made)
(2 real changes made)

. capture drop gg_*

. gen gg_hashtag = strpos(text, "#")>0

. gen gg_at = strpos(text, "@")>0

. gen gg_reply = in_reply_to_user!=""

. *at zero for this dataset
. gen gg_rt = strpos(text, "rt ") == 1

. gen gg_http = strpos(text, "http")>0

. 
. ***merge MP data****
. 
. merge m:m screen_name using "${PathData}mp.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             1
        from master                         1  (_merge==1)
        from using                          0  (_merge==2)

    matched                           322,219  (_merge==3)
    -----------------------------------------

. rename _merge mergeMP

. 
. 
. ***** Coding VARIABLES (general) *****
. 
. gen dOfWeek = mod(date2,7)
(2 missing values generated)

. replace dOfWeek = dOfWeek - 2
(322,218 real changes made)

. replace dOfWeek = dOfWeek + 7 if dOfWeek<1
(138,041 real changes made)

. 
. drop gg_rt

. gen gg_rt = strpos(text, "rt ") == 1

. 
. gen gg_adamowicz = strpos(text, "adamowicz")>0

. 
. gen log_rt = log(rt_count + 1)
(2 missing values generated)

. gen log_fav = log(favcount + 1)
(2 missing values generated)

. gen log_engagement = log(rt_count + favcount + 1)
(2 missing values generated)

. egen userid = group(screen_name)

. bysort date2 userid: gen N_du = _N

. 
. 
. 
. **** Coding TREATMENT (Adamowicz) *******
. 
.         
. gen dayC_ada = date2 - 21562
(2 missing values generated)

. gen dayC_sq_ada = dayC_ada*dayC_ada
(2 missing values generated)

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada
(2 missing values generated)

. gen post_ada = date2>=21562

. gen dayC_post_ada = dayC_ada*post_ada
(2 missing values generated)

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada
(2 missing values generated)

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada
(2 missing values generated)

. gen dayC_opos_ada = dayC_ada*opposition
(2 missing values generated)

. gen dayC_sq_opos_ada = dayC_sq_ada*opposition
(2 missing values generated)

. gen dayC_cu_opos_ada = dayC_cu_ada*opposition
(2 missing values generated)

. gen dayC_opos_post_ada = dayC_ada*opposition*post_ada
(2 missing values generated)

. gen dayC_sq_opos_post_ada = dayC_sq_ada*opposition*post_ada
(2 missing values generated)

. gen dayC_cu_opos_post_ada = dayC_cu_ada*opposition*post_ada
(2 missing values generated)

. gen post_opo_ada = post_ada*opposition

. gen post_gob_ada = post_ada*government

. 
. gen hour_sq = hour*hour
(2 missing values generated)

. gen hour_cu = hour*hour*hour
(2 missing values generated)

. 
. bysort userid: gen n_u = _n

. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21562

. foreach targetdate in 21562 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(322,218 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21562
(2 missing values generated)

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
. 
. 
. 
. **** Coding TREATMENT (Placebo) *******
. 
.         
. gen dayC_placebo = date2 - 21547
(2 missing values generated)

. gen dayC_sq_placebo = dayC_placebo*dayC_placebo
(2 missing values generated)

. gen dayC_cu_placebo = dayC_placebo*dayC_placebo*dayC_placebo
(2 missing values generated)

. gen post_placebo = date2>=21457

. gen dayC_post_placebo = dayC_placebo*post_placebo
(2 missing values generated)

. gen dayC_sq_post_placebo = dayC_sq_placebo*post_placebo
(2 missing values generated)

. gen dayC_cu_post_placebo = dayC_cu_placebo*post_placebo
(2 missing values generated)

. gen dayC_opos_placebo = dayC_placebo*opposition
(2 missing values generated)

. gen dayC_sq_opos_placebo = dayC_sq_placebo*opposition
(2 missing values generated)

. gen dayC_cu_opos_placebo = dayC_cu_placebo*opposition
(2 missing values generated)

. gen dayC_opos_post_placebo = dayC_placebo*opposition*post_placebo
(2 missing values generated)

. gen dayC_sq_opos_post_placebo = dayC_sq_placebo*opposition*post_placebo
(2 missing values generated)

. gen dayC_cu_opos_post_placebo = dayC_cu_placebo*opposition*post_placebo
(2 missing values generated)

. gen post_opo_placebo = post_placebo*opposition

. gen post_gob_placebo = post_placebo*government

. 
. 
. gen ffd_dayssinceE_placebo = 99999

. gen ffd_targetdate_placebo = 21547

. foreach targetdate in 21547 {
  2.         replace ffd_dayssinceE_placebo = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_placebo))
  3.         replace ffd_targetdate_placebo = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_placebo))
  4. }
(322,218 real changes made)
(0 real changes made)

. gen event_days_placebo = date2-21547
(2 missing values generated)

. 
. gen timeWindow0_placebo = ffd_dayssinceE_placebo>-3 & ffd_dayssinceE_placebo<2

. gen timeWindow1_placebo = ffd_dayssinceE_placebo>-6 & ffd_dayssinceE_placebo<5

. gen timeWindow2_placebo = ffd_dayssinceE_placebo>-11 & ffd_dayssinceE_placebo<10

. gen timeWindow3_placebo = ffd_dayssinceE_placebo>-16 & ffd_dayssinceE_placebo<15

. gen timeWindow4_placebo = ffd_dayssinceE_placebo>-21 & ffd_dayssinceE_placebo<20

. gen timeWindow5_placebo = ffd_dayssinceE_placebo>-31 & ffd_dayssinceE_placebo<30

. 
. 
. 
. 
. **** Coding TREATMENT (WOSP) *******
. 
. 
. gen dayC_wosp = date2 - 21198
(2 missing values generated)

. gen dayC_sq_wosp = dayC_wosp*dayC_wosp
(2 missing values generated)

. gen dayC_cu_wosp = dayC_wosp*dayC_wosp*dayC_wosp
(2 missing values generated)

. gen post_wosp = date2>=21198

. gen dayC_post_wosp = dayC_wosp*post_wosp
(2 missing values generated)

. gen dayC_sq_post_wosp = dayC_sq_wosp*post_wosp
(2 missing values generated)

. gen dayC_cu_post_wosp = dayC_cu_wosp*post_wosp
(2 missing values generated)

. gen dayC_opos_wosp = dayC_wosp*opposition
(2 missing values generated)

. gen dayC_sq_opos_wosp = dayC_sq_wosp*opposition
(2 missing values generated)

. gen dayC_cu_opos_wosp = dayC_cu_wosp*opposition
(2 missing values generated)

. gen dayC_opos_post_wosp = dayC_wosp*opposition*post_wosp
(2 missing values generated)

. gen dayC_sq_opos_post_wosp = dayC_sq_wosp*opposition*post_wosp
(2 missing values generated)

. gen dayC_cu_opos_post_wosp = dayC_cu_wosp*opposition*post_wosp
(2 missing values generated)

. gen post_opo_wosp = post_wosp*opposition

. gen post_gob_wosp = post_wosp*government

. 
. 
. gen ffd_dayssinceE_wosp = 99999

. gen ffd_targetdate_wosp = 21198

. foreach targetdate in 21198 {
  2.         replace ffd_dayssinceE_wosp = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_wosp))
  3.         replace ffd_targetdate_wosp = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_wosp))
  4. }
(322,218 real changes made)
(0 real changes made)

. gen event_days_wosp = date2-21198
(2 missing values generated)

. 
. gen timeWindow0_wosp = ffd_dayssinceE_wosp>-3 & ffd_dayssinceE_wosp<2

. gen timeWindow1_wosp = ffd_dayssinceE_wosp>-6 & ffd_dayssinceE_wosp<5

. gen timeWindow2_wosp = ffd_dayssinceE_wosp>-11 & ffd_dayssinceE_wosp<10

. gen timeWindow3_wosp = ffd_dayssinceE_wosp>-16 & ffd_dayssinceE_wosp<15

. gen timeWindow4_wosp = ffd_dayssinceE_wosp>-21 & ffd_dayssinceE_wosp<20

. gen timeWindow5_wosp = ffd_dayssinceE_wosp>-31 & ffd_dayssinceE_wosp<30

. 
. 
. 
. **** Coding OTHER MENTIONS *******
. 
. 
. replace text = " " + text
(322,220 real changes made)

. gen opMentions = strpos(text," rzd") + strpos(text," min ") + strpos(text,"minist") + strpos(text,"wicemin") + strpos(text,"pis ") +
>  strpos(text,"tvp") + strpos(text,"andruszkiewicz") + strpos(text,"berni_krynick") + strpos(text,"budet") + strpos(text,"premier") +
>  strpos(text,"morawieck") + strpos(text,"pisowsk") + strpos(text,"macierewicz") + strpos(text,"kaczysk") + strpos(text," msz ") + st
> rpos(text,"tygodnik_sieci") + strpos(text,"smolesk") + strpos(text,"misiewicz") + strpos(text,"tasmykaczynsk") + strpos(text,"glapis
> ki") + strpos(text,"glapinski") + strpos(text,"szydo") + strpos(text,"ziobro") + strpos(text,"zieliski") + strpos(text,"gowin") + st
> rpos(text,"jbrudzinski") + strpos(text,"drelich") + strpos(text,"terlecki") + strpos(text,"sdownictw") + strpos(text,"pisorgpl") + s
> trpos(text," nbp ") + strpos(text,"wicemarsz") + strpos(text," cba ") + strpos(text,"patryk jaki") + strpos(text,"policj") + strpos(
> text,"czaputowicz") + strpos(text,"gliski")

. replace opMentions = opMentions>0
(104,106 real changes made)

. gen govMentions = strpos(text," opozycj") + strpos(text,"bzdrojewski") + strpos(text," lewic") + strpos(text,"olejnik") + strpos(tex
> t," lewicow") + strpos(text," biedro") + strpos(text," lewac") + strpos(text," psl_") + strpos(text," psl ") + strpos(text,"gazwyb")
>  + strpos(text," nowick") + strpos(text," sikorsk") + strpos(text,"schetyn") + strpos(text," neuman") + strpos(text,"tomasz_lis") + 
> strpos(text,"tomasz lis") + strpos(text," lis_tomasz") + strpos(text," tusk") + strpos(text," platform") + strpos(text,"trzaskowski_
> ") + strpos(text,"platforma_org") + strpos(text,"klubnauer") + strpos(text," gw_") + strpos(text,"gazetawyborcza") + strpos(text,"tv
> n24")

. replace govMentions = govMentions>0
(38,984 real changes made)

. replace govMentions = govMentions + strpos(text,"platforma_org")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(3,824 real changes made)

. replace govMentions = govMentions + strpos(text,"arlukowicz")>0
(2,212 real changes made)

. replace govMentions = govMentions + strpos(text,"bbudka")>0
(2,247 real changes made)

. replace govMentions = govMentions + strpos(text,"andrzejhalicki")>0
(1,381 real changes made)

. replace govMentions = govMentions + strpos(text,"slawekneumann")>0
(1,764 real changes made)

. replace govMentions = govMentions + strpos(text,"schetynadlapo")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mkierwinski")>0
(2,090 real changes made)

. replace govMentions = govMentions + strpos(text,"jangrabiec")>0
(1,611 real changes made)

. replace govMentions = govMentions + strpos(text,"achybicka")>0
(195 real changes made)

. replace govMentions = govMentions + strpos(text,"protasiewiczj")>0
(136 real changes made)

. replace govMentions = govMentions + strpos(text,"zpawlowicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"trzaskowski_")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszlenz")>0
(105 real changes made)

. replace govMentions = govMentions + strpos(text,"hannagw")>0
(335 real changes made)

. replace govMentions = govMentions + strpos(text,"asia_mucha")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"niesiolowskis")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"bbukiewicz")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"c_grabarczyk")>0
(46 real changes made)

. replace govMentions = govMentions + strpos(text,"ikatarasinska")>0
(133 real changes made)

. replace govMentions = govMentions + strpos(text,"ctomczyk")>0
(1,253 real changes made)

. replace govMentions = govMentions + strpos(text,"sowamarek")>0
(286 real changes made)

. replace govMentions = govMentions + strpos(text,"ireneuszras")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"kopacz_ewa")>0
(33 real changes made)

. replace govMentions = govMentions + strpos(text,"m_k_blonska")>0
(1,727 real changes made)

. replace govMentions = govMentions + strpos(text,"adam_korol")>0
(190 real changes made)

. replace govMentions = govMentions + strpos(text,"pomaska")>0
(1,267 real changes made)

. replace govMentions = govMentions + strpos(text,"henryka_henia50")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"miroslawanykiel")>0
(95 real changes made)

. replace govMentions = govMentions + strpos(text,"marianzembala")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"urszulaaugustyn")>0
(248 real changes made)

. replace govMentions = govMentions + strpos(text,"kr_szumilas")>0
(430 real changes made)

. replace govMentions = govMentions + strpos(text,"okladrewnowicz")>0
(464 real changes made)

. replace govMentions = govMentions + strpos(text,"mswitczak")>0
(854 real changes made)

. replace govMentions = govMentions + strpos(text,"jakubrutnicki")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"mjanyska")>0
(317 real changes made)

. replace govMentions = govMentions + strpos(text,"ziolkowskiszym")>0
(152 real changes made)

. replace govMentions = govMentions + strpos(text,"waldydzikowski")>0
(122 real changes made)

. replace govMentions = govMentions + strpos(text,"slawomirnitras")>0
(1,367 real changes made)

. replace govMentions = govMentions + strpos(text,"krzysztofbrejza")>0
(2,244 real changes made)

. replace govMentions = govMentions + strpos(text,"arkadiuszmyrcha")>0
(1,171 real changes made)

. replace govMentions = govMentions + strpos(text,"mwielichowska")>0
(1,181 real changes made)

. replace govMentions = govMentions + strpos(text,"gajewska_kinga")>0
(421 real changes made)

. replace govMentions = govMentions + strpos(text,"newsplatforma")>0
(2,752 real changes made)

. replace govMentions = govMentions + strpos(text,"grzegorzfurgo")>0
(287 real changes made)

. replace govMentions = govMentions + strpos(text,"bborusewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"rtyszkiewicz")>0
(927 real changes made)

. replace govMentions = govMentions + strpos(text,"jaroslawduda")>0
(253 real changes made)

. replace govMentions = govMentions + strpos(text,"zdzislaw_gawlik")>0
(91 real changes made)

. replace govMentions = govMentions + strpos(text,"arturgierada")>0
(23 real changes made)

. replace govMentions = govMentions + strpos(text,"a_marchewka")>0
(164 real changes made)

. replace govMentions = govMentions + strpos(text,"jacek_protas")>0
(143 real changes made)

. replace govMentions = govMentions + strpos(text,"wojciechsaluga")>0
(206 real changes made)

. replace govMentions = govMentions + strpos(text,"wslugocki")>0
(511 real changes made)

. replace govMentions = govMentions + strpos(text,"hannazdanowska")>0
(113 real changes made)

. replace govMentions = govMentions + strpos(text,"prezydentzuk")>0
(19 real changes made)

. replace govMentions = govMentions + strpos(text,"izabela_debska")>0
(6 real changes made)

. replace govMentions = govMentions + strpos(text,"tadeuszzwiefka")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"donaldtusk")>0
(651 real changes made)

. replace opMentions = opMentions + strpos(text,"piotr_naimski")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"pisorgpl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"morawieckim")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jbrudzinski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"patrykjaki")>0
(999 real changes made)

. replace opMentions = opMentions + strpos(text,"andzyberto")>0
(783 real changes made)

. replace opMentions = opMentions + strpos(text,"kurskipl")>0
(190 real changes made)

. replace opMentions = opMentions + strpos(text,"piotrglinski")>0
(583 real changes made)

. replace opMentions = opMentions + strpos(text,"jaroslaw_gowin")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"stkarczewski")>0
(382 real changes made)

. replace opMentions = opMentions + strpos(text,"marekkuchcinski")>0
(593 real changes made)

. replace opMentions = opMentions + strpos(text,"beataszydlo")>0
(747 real changes made)

. replace opMentions = opMentions + strpos(text,"beatamk")>0
(761 real changes made)

. replace opMentions = opMentions + strpos(text,"e_rafalska")>0
(178 real changes made)

. replace opMentions = opMentions + strpos(text,"krystpawlowicz")>0
(126 real changes made)

. replace opMentions = opMentions + strpos(text,"ziobropl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"beatakempa_kprm")>0
(222 real changes made)

. replace opMentions = opMentions + strpos(text,"mblaszczak")>0
(485 real changes made)

. replace opMentions = opMentions + strpos(text,"macierewicz_a")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"elzbietawitek")>0
(76 real changes made)

. replace opMentions = opMentions + strpos(text,"_annazalewska")>0
(113 real changes made)

. replace opMentions = opMentions + strpos(text,"michaldworczyk")>0
(255 real changes made)

. replace opMentions = opMentions + strpos(text,"latostomasz")>0
(16 real changes made)

. replace opMentions = opMentions + strpos(text,"slawzawislak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"wassermann_ma")>0
(237 real changes made)

. replace opMentions = opMentions + strpos(text,"arekmularczyk")>0
(525 real changes made)

. replace opMentions = opMentions + strpos(text,"w_bernacki")>0
(70 real changes made)

. replace opMentions = opMentions + strpos(text,"mareksuski")>0
(47 real changes made)

. replace opMentions = opMentions + strpos(text,"akosztowniak")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"minenergii")>0
(161 real changes made)

. replace opMentions = opMentions + strpos(text,"a_czartoryski")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"mariuszkaminsk")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"sasinjacek ?")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"bogdan_rzonca")>0
(20 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejszlachta")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mrirw_gov_pl")>0
(131 real changes made)

. replace opMentions = opMentions + strpos(text,"d_piontkowski")>0
(38 real changes made)

. replace opMentions = opMentions + strpos(text,"mkidn_gov_pl")>0
(430 real changes made)

. replace opMentions = opMentions + strpos(text,"stanislaw_szwed")>0
(139 real changes made)

. replace opMentions = opMentions + strpos(text,"szymongizynski")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"izabelakloc")>0
(44 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejduda")>0
(1,784 real changes made)

. replace opMentions = opMentions + strpos(text,"annamkrupka")>0
(65 real changes made)

. replace opMentions = opMentions + strpos(text,"iwonaarent")>0
(10 real changes made)

. replace opMentions = opMentions + strpos(text,"joannalichocka")>0
(148 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldczarneck3")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"tadeuszdziuba")>0
(10 real changes made)

. replace opMentions = opMentions + strpos(text,"zagorskimarek")>0
(165 real changes made)

. replace opMentions = opMentions + strpos(text,"marekgrobarczyk")>0
(96 real changes made)

. replace opMentions = opMentions + strpos(text,"amadamczyk")>0
(615 real changes made)

. replace opMentions = opMentions + strpos(text,"jerzykwiecinski")>0
(550 real changes made)

. replace opMentions = opMentions + strpos(text,"jemilewicz")>0
(328 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldbanka")>0
(205 real changes made)

. replace opMentions = opMentions + strpos(text,"jczaputowicz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szumowskilukasz")>0
(114 real changes made)

. replace opMentions = opMentions + strpos(text,"j_kopcinska")>0
(56 real changes made)

. replace opMentions = opMentions + strpos(text,"profkarski")>0
(15 real changes made)

. replace opMentions = opMentions + strpos(text,"ryszardterlecki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"r_czarnecki")>0
(627 real changes made)

. replace opMentions = opMentions + strpos(text,"grzegorzczelej")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"anita_cz")>0
(39 real changes made)

. replace opMentions = opMentions + strpos(text,"ministerjurgiel")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"cymanskitadeusz")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"jacekzalek")>0
(84 real changes made)

. gen mentionOther = (opMentions & opposition) | (govMentions & government)

. replace mentionOther = 0 if mentionOther == .
(0 real changes made)

. 
. 
. **** Coding other VARIABLES *******
. 
. 
. gen post_mention_ada = post_ada*mentionOther

. gen post_opo_mention_ada = post_ada*opposition*mentionOther

. gen opo_mention = opposition*mentionOther

. 
. gen mention_ada = mentionOther*gg_adamowicz

. gen opo_ada = opposition*gg_adamowicz

. gen mention_opo_ada= mentionOther*gg_adamowicz*opposition

. 
. 
. 
. drop n_u

. bysort userid: gen n_u=_n

. bysort userid: gen N_u=_N

. drop if N_u<10
(6 observations deleted)

. reg log_engagement i.userid if !post_ada

      Source |       SS           df       MS      Number of obs   =   169,169
-------------+----------------------------------   F(102, 169066)  =    808.50
       Model |  178671.544       102  1751.68181   Prob > F        =    0.0000
    Residual |  366294.249   169,066  2.16657547   R-squared       =    0.3279
-------------+----------------------------------   Adj R-squared   =    0.3275
       Total |  544965.793   169,168  3.22144728   Root MSE        =    1.4719

------------------------------------------------------------------------------
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      userid |
          2  |  -2.126437   .0874388   -24.32   0.000    -2.297815   -1.955059
          3  |  -.7426658   .0698873   -10.63   0.000    -.8796433   -.6056883
          4  |  -.4394563   .0770939    -5.70   0.000    -.5905586   -.2883539
          5  |  -.3483686   .0694853    -5.01   0.000    -.4845583   -.2121789
          6  |   .7453109   .0688633    10.82   0.000     .6103403    .8802815
          7  |  -.2520556   .0703011    -3.59   0.000    -.3898443    -.114267
          8  |   .3342067   .0696509     4.80   0.000     .1976925    .4707208
          9  |  -.3009879   .0712476    -4.22   0.000    -.4406316   -.1613443
         10  |   .7746646   .0743212    10.42   0.000     .6289966    .9203326
         11  |   .6216429   .0732156     8.49   0.000     .4781419    .7651438
         12  |   .0707033   .0828433     0.85   0.393    -.0916678    .2330745
         13  |   .9663776   .0739941    13.06   0.000     .8213507    1.111404
         14  |  -.7561312   .0713726   -10.59   0.000    -.8960198   -.6162425
         15  |    1.43484   .0815164    17.60   0.000      1.27507    1.594611
         16  |    .807862   .0876122     9.22   0.000     .6361441      .97958
         17  |   .2647819   .0771157     3.43   0.001     .1136367     .415927
         18  |   .9910289   .0693364    14.29   0.000      .855131    1.126927
         19  |  -2.757026   .2853284    -9.66   0.000    -3.316263   -2.197788
         20  |  -.9259739   .0743558   -12.45   0.000     -1.07171   -.7802382
         21  |  -1.154458   .0814779   -14.17   0.000    -1.314153   -.9947635
         23  |  -.2459719   .0696179    -3.53   0.000    -.3824215   -.1095224
         24  |   1.317421   .0690473    19.08   0.000     1.182089    1.452752
         25  |   1.377305   .0705131    19.53   0.000     1.239101    1.515509
         26  |   .7437654   .0697224    10.67   0.000      .607111    .8804197
         27  |  -.1055793   .0707357    -1.49   0.136    -.2442198    .0330612
         28  |  -.5725141   .0705507    -8.11   0.000    -.7107919   -.4342363
         29  |   .7785042   .0723711    10.76   0.000     .6366585      .92035
         31  |   1.478376   .0962042    15.37   0.000     1.289817    1.666934
         32  |   .4473846   .0788583     5.67   0.000      .292824    .6019453
         33  |  -1.322722   .0847296   -15.61   0.000    -1.488791   -1.156654
         34  |  -.8861229   .0704959   -12.57   0.000    -1.024293   -.7479525
         35  |  -.8350314   .0704307   -11.86   0.000    -.9730739   -.6969888
         36  |   .7988161   .0717213    11.14   0.000     .6582439    .9393884
         37  |  -1.390499   .0699016   -19.89   0.000    -1.527504   -1.253493
         38  |   .4131894   .0708194     5.83   0.000     .2743849    .5519938
         39  |   .7197369   .0693886    10.37   0.000     .5837368     .855737
         40  |   1.258723   .0695487    18.10   0.000     1.122409    1.395037
         41  |   1.414364   .0703888    20.09   0.000     1.276403    1.552324
         42  |  -.1649705   .3527037    -0.47   0.640     -.856262    .5263209
         43  |  -.8782338   .0689625   -12.73   0.000    -1.013399   -.7430689
         44  |   -1.61177   .0689073   -23.39   0.000    -1.746827   -1.476713
         45  |   4.258728   .1065556    39.97   0.000     4.049881    4.467574
         47  |   .5610087   .1960221     2.86   0.004     .1768097    .9452078
         48  |   .2448497   .0695766     3.52   0.000      .108481    .3812183
         49  |   2.492353   .0691535    36.04   0.000     2.356814    2.627892
         50  |  -.0212036   .0731715    -0.29   0.772    -.1646181    .1222109
         52  |  -.4514759   .0690063    -6.54   0.000    -.5867268    -.316225
         53  |   1.152534   .0703777    16.38   0.000     1.014596    1.290473
         54  |   .6742392   .0895801     7.53   0.000     .4986641    .8498143
         56  |   1.809716    .072962    24.80   0.000     1.666712     1.95272
         57  |   1.898863   .3734241     5.09   0.000      1.16696    2.630766
         58  |   1.005095   .0718952    13.98   0.000     .8641815    1.146008
         59  |   1.584415   .0795441    19.92   0.000      1.42851     1.74032
         60  |    .299272   .0700286     4.27   0.000     .1620175    .4365265
         61  |   .7063662   .0690473    10.23   0.000      .571035    .8416975
         62  |  -.4930616   .0690744    -7.14   0.000    -.6284459   -.3576773
         63  |  -.5516278   .1109232    -4.97   0.000    -.7690349   -.3342207
         64  |  -.9253282   .1037453    -8.92   0.000    -1.128667   -.7219897
         65  |  -1.062254   .0764007   -13.90   0.000    -1.211998   -.9125107
         66  |  -1.524261   .2217467    -6.87   0.000     -1.95888   -1.089643
         67  |   .2404554   .0696201     3.45   0.001     .1040016    .3769092
         68  |   .4052999   .1289611     3.14   0.002     .1525391    .6580608
         69  |   .1134881   .0784112     1.45   0.148    -.0401962    .2671724
         70  |   -.102867   .0687565    -1.50   0.135    -.2376282    .0318942
         71  |  -.4506584   .0836083    -5.39   0.000    -.6145289   -.2867879
         72  |  -.1869655   .0697634    -2.68   0.007    -.3237001   -.0502308
         73  |   1.245091   .3436057     3.62   0.000     .5716311     1.91855
         74  |  -.8100964    .068802   -11.77   0.000    -.9449468    -.675246
         75  |  -.0877045   .0719618    -1.22   0.223    -.2287481    .0533391
         76  |  -.1303431   .1000156    -1.30   0.192    -.3263715    .0656852
         77  |   .7378811   .0805152     9.16   0.000     .5800731    .8956892
         78  |  -1.153842   .0718787   -16.05   0.000    -1.294723   -1.012961
         79  |  -.9096144   .1286785    -7.07   0.000    -1.161821   -.6574074
         80  |  -.2185753   .1727054    -1.27   0.206    -.5570741    .1199235
         81  |   .9463642   .0713042    13.27   0.000     .8066096    1.086119
         82  |   .4726417   .1616864     2.92   0.003     .1557398    .7895436
         83  |    .855669     .06945    12.32   0.000     .7195486    .9917894
         84  |  -1.747605   .0688217   -25.39   0.000    -1.882494   -1.612715
         87  |  -2.248234   .0686215   -32.76   0.000     -2.38273   -2.113737
         88  |   1.804145   .0699305    25.80   0.000     1.667082    1.941207
         89  |   1.110634    .121711     9.13   0.000     .8720834    1.349185
         90  |   2.359074   .0904716    26.08   0.000     2.181751    2.536396
         91  |   .8855563   .0935671     9.46   0.000     .7021668    1.068946
         92  |   .7595775   .0695723    10.92   0.000     .6232172    .8959377
         93  |   .6572004   .0687436     9.56   0.000     .5224644    .7919364
         95  |  -.9082225   .0697405   -13.02   0.000    -1.044912   -.7715325
         96  |   .5329965    .071059     7.50   0.000     .3937224    .6722705
         97  |    .081457   .0744607     1.09   0.274    -.0644844    .2273984
         98  |   .7831579    .074079    10.57   0.000     .6379648    .9283511
         99  |   1.490792    .307097     4.85   0.000     .8888881    2.092695
        100  |     .22142   .0712758     3.11   0.002     .0817211     .361119
        101  |  -1.477234   .0740855   -19.94   0.000     -1.62244   -1.332028
        102  |   .0523966   .1158176     0.45   0.651    -.1746033    .2793964
        103  |   .6477995   .0701973     9.23   0.000     .5102144    .7853846
        104  |   .5368314     .07081     7.58   0.000     .3980453    .6756174
        105  |  -2.613995   .3527037    -7.41   0.000    -3.305287   -1.922704
        106  |   1.393126   .0725356    19.21   0.000     1.250958    1.535294
        108  |   1.199927   .0889404    13.49   0.000     1.025605    1.374248
        109  |   -1.61509   .2853284    -5.66   0.000    -2.174327   -1.055852
        110  |   .2468688   .0715242     3.45   0.001     .1066829    .3870547
        111  |  -1.853105    .068825   -26.92   0.000       -1.988   -1.718209
        112  |     1.2417   .0824505    15.06   0.000     1.080099    1.403301
             |
       _cons |   2.806536   .0635184    44.18   0.000     2.682041    2.931031
------------------------------------------------------------------------------

. predict popularity
(option xb assumed; fitted values)

. egen popRankT = rank(-popularity) if n_u==1 & opposition, unique
(322165 missing values generated)

. bysort userid: egen popRankO = mean(popRankT)
(147394 missing values generated)

. drop popRankT

. egen popRankT = rank(-popularity) if n_u==1 & government, unique
(322154 missing values generated)

. bysort userid: egen popRankG = mean(popRankT)
(174820 missing values generated)

. drop popRankT

. gen popRankWithin = popRankG if government
(174,820 missing values generated)

. replace popRankWithin = popRankO if opposition
(174,820 real changes made)

. egen popRankT = rank(-popularity) if n_u==1, unique
(322105 missing values generated)

. bysort userid: egen popRank = mean(popRankT)

. drop popRankT

. 
. reg mentionOther i.userid if post_ada

      Source |       SS           df       MS      Number of obs   =   153,045
-------------+----------------------------------   F(101, 152943)  =    209.74
       Model |   2936.0189       101  29.0694941   Prob > F        =    0.0000
    Residual |  21197.3373   152,943  .138596322   R-squared       =    0.1217
-------------+----------------------------------   Adj R-squared   =    0.1211
       Total |  24133.3562   153,044  .157689006   Root MSE        =    .37229

------------------------------------------------------------------------------
mentionOther |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      userid |
          2  |   .0073436   .0378539     0.19   0.846    -.0668492    .0815365
          3  |    -.06891   .0221637    -3.11   0.002    -.1123503   -.0254696
          4  |   .1410255   .0320389     4.40   0.000     .0782299     .203821
          5  |  -.0184648   .0240537    -0.77   0.443    -.0656096    .0286801
          6  |  -.0599049   .0319978    -1.87   0.061    -.1226198    .0028101
          7  |   .0563406   .0221401     2.54   0.011     .0129464    .0997347
          8  |  -.0036599   .0260923    -0.14   0.888    -.0548003    .0474805
          9  |  -.0024089   .0315651    -0.08   0.939    -.0642758     .059458
         10  |   .1946824   .0212626     9.16   0.000     .1530081    .2363567
         11  |   .0395796   .0215742     1.83   0.067    -.0027054    .0818646
         12  |  -.0234912    .024258    -0.97   0.333    -.0710363     .024054
         13  |  -.0229483   .0333803    -0.69   0.492    -.0883731    .0424765
         14  |   .0024665    .025106     0.10   0.922    -.0467407    .0516736
         15  |   .3305327   .0212742    15.54   0.000     .2888356    .3722298
         16  |  -.0249607   .0252153    -0.99   0.322    -.0743822    .0244607
         17  |  -.0545434   .0290931    -1.87   0.061    -.1115653    .0024784
         18  |   .2288207     .02378     9.62   0.000     .1822124    .2754289
         20  |  -.0232246   .0344527    -0.67   0.500    -.0907512    .0443021
         21  |   .0560839   .0699437     0.80   0.423    -.0810042    .1931721
         22  |   .0878116   .0211325     4.16   0.000     .0463923     .129231
         23  |  -.0504004   .0223471    -2.26   0.024    -.0942002   -.0066006
         24  |  -.0237176   .0291156    -0.81   0.415    -.0807835    .0333483
         25  |   .3528347    .022004    16.03   0.000     .3097072    .3959622
         26  |   .0988419    .023379     4.23   0.000     .0530195    .1446643
         27  |   -.034551   .0218886    -1.58   0.114    -.0774522    .0083502
         28  |  -.0605931   .0217894    -2.78   0.005    -.1032999   -.0178864
         29  |  -.0033831   .0215848    -0.16   0.875    -.0456888    .0389226
         30  |   .0176851   .0210566     0.84   0.401    -.0235854    .0589557
         31  |   .2120525   .0210793    10.06   0.000     .1707375    .2533675
         32  |  -.0559992   .0399476    -1.40   0.161    -.1342956    .0222973
         33  |  -.0123423   .0426759    -0.29   0.772    -.0959861    .0713015
         34  |   .0637704   .0223295     2.86   0.004      .020005    .1075358
         35  |  -.0686897   .0217927    -3.15   0.002     -.111403   -.0259764
         36  |   .3457275   .0217786    15.87   0.000      .303042    .3884131
         37  |  -.0658662   .0248511    -2.65   0.008    -.1145738   -.0171585
         38  |   .1262704    .022112     5.71   0.000     .0829314    .1696094
         39  |   .0275542   .0255621     1.08   0.281     -.022547    .0776553
         40  |  -.0197922   .0231598    -0.85   0.393     -.065185    .0256006
         41  |  -.0527208    .024423    -2.16   0.031    -.1005893   -.0048522
         43  |  -.0715517   .0247959    -2.89   0.004    -.1201511   -.0229523
         44  |   .0710914   .0255094     2.79   0.005     .0210935    .1210893
         45  |  -.0729483   .0284135    -2.57   0.010    -.1286382   -.0172584
         46  |   .2240768   .0210414    10.65   0.000     .1828361    .2653175
         48  |   .1180534   .0232146     5.09   0.000     .0725532    .1635536
         49  |  -.0075798   .0247796    -0.31   0.760    -.0561472    .0409877
         50  |  -.0425135   .0249425    -1.70   0.088    -.0914004    .0063733
         51  |   .2079944   .0210451     9.88   0.000     .1667464    .2492424
         52  |   .0881996   .0228746     3.86   0.000     .0433657    .1330334
         53  |   .1139826   .0222598     5.12   0.000     .0703538    .1576114
         54  |    .008447   .0350294     0.24   0.809    -.0602099    .0771039
         56  |   .1113335   .0282288     3.94   0.000     .0560056    .1666614
         57  |  -.0495108   .0387821    -1.28   0.202    -.1255229    .0265012
         58  |   .2383479   .0222129    10.73   0.000      .194811    .2818847
         59  |   .2062725   .0211598     9.75   0.000     .1647998    .2477451
         60  |    .181289   .0229792     7.89   0.000     .1362503    .2263277
         61  |  -.0495205   .0243344    -2.04   0.042    -.0972153   -.0018256
         62  |  -.0627269   .0256393    -2.45   0.014    -.1129794   -.0124743
         63  |  -.0559512   .0285287    -1.96   0.050    -.1118669   -.0000354
         64  |   .0179608   .0502116     0.36   0.721     -.080453    .1163746
         65  |  -.0581335    .030571    -1.90   0.057     -.118052     .001785
         67  |    .208917   .0237674     8.79   0.000     .1623334    .2555005
         68  |   .3420177   .0210775    16.23   0.000     .3007062    .3833291
         69  |   .2787758   .0272007    10.25   0.000      .225463    .3320886
         70  |   .0494145   .0248233     1.99   0.047     .0007614    .0980677
         71  |  -.0179585   .0265243    -0.68   0.498    -.0699456    .0340286
         72  |  -.0697225   .0294678    -2.37   0.018    -.1274788   -.0119663
         73  |   .0236034   .0371093     0.64   0.525    -.0491301    .0963369
         74  |   .1623458    .030014     5.41   0.000     .1035191    .2211725
         75  |  -.0629065   .0231786    -2.71   0.007     -.108336    -.017477
         76  |   .1951482   .0281574     6.93   0.000     .1399603     .250336
         77  |  -.0259398   .0267845    -0.97   0.333    -.0784369    .0265574
         78  |   .1873256   .0238183     7.86   0.000     .1406423     .234009
         80  |   .0020517   .0623392     0.03   0.974    -.1201318    .1242352
         81  |   .2683006   .0233058    11.51   0.000     .2226216    .3139795
         82  |  -.0729483   .3728506    -0.20   0.845    -.8037279    .6578312
         83  |  -.0368952   .0235109    -1.57   0.117     -.082976    .0091856
         84  |    .275037   .0304786     9.02   0.000     .2152996    .3347744
         86  |   .2943606   .0210428    13.99   0.000     .2531171     .335604
         87  |   .0258171   .0461772     0.56   0.576    -.0646892    .1163234
         88  |   .0143957   .0233408     0.62   0.537    -.0313517    .0601431
         89  |   .2107141   .0258861     8.14   0.000     .1599779    .2614504
         90  |  -.0051517   .0526342    -0.10   0.922    -.1083136    .0980102
         91  |   -.052115    .026646    -1.96   0.050    -.1043406    .0001106
         92  |   .1198334   .0233081     5.14   0.000     .0741499    .1655168
         93  |   .1259467   .0344527     3.66   0.000       .05842    .1934734
         95  |   .1566918   .0220656     7.10   0.000     .1134438    .1999399
         96  |  -.0729483    .031271    -2.33   0.020    -.1342389   -.0116578
         97  |   .1315051    .026492     4.96   0.000     .0795814    .1834288
         98  |  -.0263978   .0213155    -1.24   0.216    -.0681758    .0153801
        100  |   .2498443   .0218674    11.43   0.000     .2069846     .292704
        101  |   -.030019   .0277715    -1.08   0.280    -.0844506    .0244125
        102  |   .1413374   .0490022     2.88   0.004     .0452941    .2373806
        103  |   -.062118   .0228328    -2.72   0.007    -.1068697   -.0173663
        104  |  -.0249778   .0243257    -1.03   0.305    -.0726558    .0227001
        106  |   .3356241   .0216604    15.49   0.000     .2931701     .378078
        107  |  -.0438885   .0210429    -2.09   0.037    -.0851321   -.0026449
        108  |   .2530195   .0210952    11.99   0.000     .2116734    .2943656
        109  |  -.0563737   .0344527    -1.64   0.102    -.1239004    .0111529
        110  |  -.0270959   .0218869    -1.24   0.216    -.0699938    .0158021
        111  |    .052766   .0285877     1.85   0.065    -.0032653    .1087972
        112  |  -.0729483   .0511977    -1.42   0.154    -.1732948    .0273982
             |
       _cons |   .0729483   .0205248     3.55   0.000     .0327202    .1131764
------------------------------------------------------------------------------

. predict mentioner
(option xb assumed; fitted values)

. 
. sum mentioner if n_u==1 & opposition, det

                        Fitted values
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0677966       .0677966
 5%     .0729483       .0729483
10%     .0729483       .0729483       Obs                  49
25%     .1367188       .0729483       Sum of Wgt.          49

50%     .2296402                      Mean           .2300285
                        Largest       Std. Dev.      .1066639
75%      .301769       .4085724
90%      .403481        .414966       Variance       .0113772
95%      .414966       .4186758       Skewness        .124098
99%      .425783        .425783       Kurtosis       1.984617

. 
. 
. label variable post_opo_ada "Post x opposition"

. label variable opposition "Opposition"

. label variable post_ada "Post"

. 
. 
. label variable mentionOther "Mention rival"

. label variable post_mention_ada "Post x Mention rival"

. 
. bysort userid: egen meanNGallT = mean(mentionOther) if !post_ada
(153045 missing values generated)

. bysort userid: egen meanNGall = mean(meanNGallT)
(37473 missing values generated)

. drop meanNGallT

. 
. gen post_ngA = post_ada*mentioner

. label variable post_ngA "Post x Negative campaigner"

. 
. 
. bysort userid: egen meanPopT = mean(log_engagement) if !post_ada
(153045 missing values generated)

. bysort userid: egen meanPop = mean(meanPopT)
(37473 missing values generated)

. drop meanPopT

. 
. gen post_pop = post_ada*meanPop
(37,473 missing values generated)

. label variable post_pop "Post x popularity"

. 
. 
. gen event_days_ada_M = event_days_ada+1000
(1 missing value generated)

. replace event_days_ada_M=. if event_days_ada_M<0
(21,669 real changes made, 21,669 to missing)

. bysort userid date2: gen n_tweets=_N

. bysort userid date2: gen _tweets=_n

. gen log_n_tweets=ln(n_tweets)

. 
. gen event_days_placebo_M = event_days_placebo+1000
(1 missing value generated)

. replace event_days_placebo_M=. if event_days_placebo_M<0
(21,148 real changes made, 21,148 to missing)

. 
. gen event_days_wosp_M = event_days_wosp+1000
(1 missing value generated)

. replace event_days_wosp_M=. if event_days_wosp_M<0
(8,825 real changes made, 8,825 to missing)

. 
. 
. gen post_mp_ada = post_ada*mp

. gen post_opo_mp = post_opo_ada*mp

. 
. label variable post_mp_ada "Post x MP"

. label variable post_opo_mp "Post x MP x opposition"

. 
. 
. 
. 
. ***** TABLES *******
. 
. 
. 
. 
. ***Table 1 **** 
. 
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3
> _ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =      19.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7372
                                                  Adj R-squared   =     0.7289
                                                  Within R-sq.    =     0.4056
Number of clusters (userid)  =         86         Root MSE        =     1.1462

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.3531998   .1829016    -1.93   0.057    -.7168572    .0104576
     hour_sq |  -.0013851   .0006615    -2.09   0.039    -.0027003   -.0000699
        hour |   .0316799   .0174704     1.81   0.073    -.0030559    .0664157
  gg_hashtag |  -.1664112   .1460111    -1.14   0.258    -.4567204     .123898
       gg_at |  -.4699707   .1118534    -4.20   0.000    -.6923651   -.2475762
    gg_reply |   -1.93847   .3160945    -6.13   0.000    -2.566951    -1.30999
       gg_rt |          0  (omitted)
     gg_http |   .3161707   .0760159     4.16   0.000     .1650307    .4673107
       _cons |   3.594422   .1800414    19.96   0.000     3.236451    3.952392
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg
> _rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =      17.17
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7258
                                                  Adj R-squared   =     0.7171
                                                  Within R-sq.    =     0.4371
Number of clusters (userid)  =         86         Root MSE        =     0.9370

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.2865203   .1084424    -2.64   0.010    -.5021328   -.0709077
     hour_sq |  -.0013801   .0005528    -2.50   0.014    -.0024792    -.000281
        hour |   .0271463   .0149153     1.82   0.072    -.0025093    .0568019
  gg_hashtag |  -.0853599   .1383964    -0.62   0.539    -.3605289    .1898091
       gg_at |  -.3502726   .0988001    -3.55   0.001    -.5467138   -.1538315
    gg_reply |  -1.715382   .2692611    -6.37   0.000    -2.250745   -1.180019
       gg_rt |          0  (omitted)
     gg_http |   .2945276   .0524297     5.62   0.000     .1902832    .3987719
       _cons |   2.288624   .1471306    15.56   0.000     1.996089    2.581159
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_r
> t, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =      17.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7319
                                                  Adj R-squared   =     0.7234
                                                  Within R-sq.    =     0.3830
Number of clusters (userid)  =         86         Root MSE        =     1.1239

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.3428801   .1862647    -1.84   0.069    -.7132243     .027464
     hour_sq |  -.0012514   .0006333    -1.98   0.051    -.0025105    7.72e-06
        hour |   .0297572   .0167981     1.77   0.080    -.0036419    .0631563
  gg_hashtag |  -.1910313   .1417598    -1.35   0.181    -.4728878    .0908252
       gg_at |  -.4439891   .1056977    -4.20   0.000    -.6541444   -.2338338
    gg_reply |  -1.833038   .3059823    -5.99   0.000    -2.441412   -1.224663
       gg_rt |          0  (omitted)
     gg_http |   .2648995   .0762267     3.48   0.001     .1133403    .4164586
       _cons |    3.42664   .1799059    19.05   0.000     3.068939    3.784341
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}Table1.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada) stats(N pFE dFE controls, fmt(0) lab
> els("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter engagement") replace nonotes 
> postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/Table1.tex)

. 
. 
. 
. ***Table 2  **** 
. 
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_repl
> y gg_rt gg_http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      34.48
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7458
                                                  Adj R-squared   =     0.7376
                                                  Within R-sq.    =     0.4249
Number of clusters (userid)  =         86         Root MSE        =     1.1277

                                    (Std. Err. adjusted for 86 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
  log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.1683723   .1910172    -0.88   0.381    -.5481657    .2114211
    mentionOther |   .7962757    .124568     6.39   0.000     .5486012     1.04395
post_mention_ada |  -.4186017   .2378032    -1.76   0.082    -.8914182    .0542147
         hour_sq |  -.0012127   .0006149    -1.97   0.052    -.0024353    9.92e-06
            hour |   .0273825   .0166546     1.64   0.104    -.0057312    .0604962
      gg_hashtag |  -.1887734   .1321114    -1.43   0.157    -.4514462    .0738994
           gg_at |  -.4804939   .1058971    -4.54   0.000    -.6910456   -.2699422
        gg_reply |  -1.833145   .3056463    -6.00   0.000    -2.440852   -1.225439
           gg_rt |          0  (omitted)
         gg_http |   .3032073   .0742092     4.09   0.000     .1556596     .450755
           _cons |   3.438485   .1767771    19.45   0.000     3.087005    3.789965
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg
> _http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      37.83
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7380
                                                  Adj R-squared   =     0.7296
                                                  Within R-sq.    =     0.4623
Number of clusters (userid)  =         86         Root MSE        =     0.9160

                                    (Std. Err. adjusted for 86 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
          log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0963798   .1227285    -0.79   0.434    -.3403969    .1476374
    mentionOther |   .7867385    .113771     6.92   0.000     .5605314    1.012946
post_mention_ada |  -.4665272   .2274111    -2.05   0.043    -.9186815    -.014373
         hour_sq |  -.0012178   .0005095    -2.39   0.019    -.0022309   -.0002047
            hour |   .0230644   .0142009     1.62   0.108    -.0051708    .0512996
      gg_hashtag |  -.1082465   .1233943    -0.88   0.383    -.3535874    .1370943
           gg_at |  -.3603488   .0940696    -3.83   0.000    -.5473845   -.1733132
        gg_reply |  -1.612253   .2575873    -6.26   0.000    -2.124406   -1.100101
           gg_rt |          0  (omitted)
         gg_http |   .2834159   .0515411     5.50   0.000     .1809383    .3858934
           _cons |   2.135988   .1465631    14.57   0.000     1.844582    2.427395
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_h
> ttp if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      30.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7401
                                                  Adj R-squared   =     0.7317
                                                  Within R-sq.    =     0.4020
Number of clusters (userid)  =         86         Root MSE        =     1.1068

                                    (Std. Err. adjusted for 86 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
         log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.1681417    .194121    -0.87   0.389    -.5541063    .2178228
    mentionOther |   .7565169   .1221037     6.20   0.000      .513742    .9992917
post_mention_ada |  -.3916481   .2299286    -1.70   0.092    -.8488078    .0655117
         hour_sq |  -.0010867   .0005907    -1.84   0.069    -.0022612    .0000878
            hour |   .0256556   .0160321     1.60   0.113    -.0062205    .0575318
      gg_hashtag |  -.2121863   .1285632    -1.65   0.103    -.4678045    .0434318
           gg_at |  -.4540237   .0996405    -4.56   0.000    -.6521357   -.2559116
        gg_reply |  -1.732865   .2959459    -5.86   0.000    -2.321284   -1.144445
           gg_rt |          0  (omitted)
         gg_http |   .2523894   .0744188     3.39   0.001     .1044249    .4003539
           _cons |   3.278326   .1765267    18.57   0.000     2.927343    3.629308
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}Table2.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada mentionOther post_mention_ada) stats(
> N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter
>  engagement: Negative campaiging effect") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/Table2.tex)

. 
. 
. 
. ***Table A4 **** 
. 
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada post_mp_ada post_opo_mp  hour_sq hour gg_hashtag gg_at gg_reply gg_
> rt gg_http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      15.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7379
                                                  Adj R-squared   =     0.7295
                                                  Within R-sq.    =     0.4071
Number of clusters (userid)  =         86         Root MSE        =     1.1450

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -1.162237   .4839531    -2.40   0.019    -2.124466   -.2000088
 post_mp_ada |  -.4675163   .3936543    -1.19   0.238    -1.250206    .3151739
 post_opo_mp |   .9529816    .510801     1.87   0.066    -.0626276    1.968591
     hour_sq |  -.0013304   .0006496    -2.05   0.044    -.0026221   -.0000388
        hour |   .0303489   .0172221     1.76   0.082    -.0038932    .0645911
  gg_hashtag |  -.1599181   .1462083    -1.09   0.277    -.4506193    .1307832
       gg_at |  -.4628524   .1138731    -4.06   0.000    -.6892627   -.2364421
    gg_reply |  -1.939611   .3161816    -6.13   0.000    -2.568264   -1.310957
       gg_rt |          0  (omitted)
     gg_http |   .3206852   .0777441     4.12   0.000     .1661091    .4752613
       _cons |   3.731791   .2305957    16.18   0.000     3.273305    4.190277
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada post_mp_ada post_opo_mp hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http 
> if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      13.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7260
                                                  Adj R-squared   =     0.7172
                                                  Within R-sq.    =     0.4376
Number of clusters (userid)  =         86         Root MSE        =     0.9369

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.9114068   .3360177    -2.71   0.008      -1.5795   -.2433135
 post_mp_ada |  -.1760135   .1934443    -0.91   0.365    -.5606327    .2086057
 post_opo_mp |   .6815449   .3584112     1.90   0.061    -.0310726    1.394162
     hour_sq |  -.0013603   .0005491    -2.48   0.015    -.0024521   -.0002684
        hour |   .0266728   .0147805     1.80   0.075    -.0027148    .0560605
  gg_hashtag |  -.0829597    .139023    -0.60   0.552    -.3593747    .1934552
       gg_at |  -.3476389   .0997356    -3.49   0.001    -.5459399   -.1493379
    gg_reply |  -1.715645   .2692328    -6.37   0.000    -2.250952   -1.180338
       gg_rt |          0  (omitted)
     gg_http |   .2964327    .053024     5.59   0.000     .1910067    .4018587
       _cons |   2.340216   .1514287    15.45   0.000     2.039135    2.641297
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada post_mp_ada post_opo_mp hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if
>  timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      13.37
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7326
                                                  Adj R-squared   =     0.7240
                                                  Within R-sq.    =     0.3847
Number of clusters (userid)  =         86         Root MSE        =     1.1226

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -1.155019   .4997079    -2.31   0.023    -2.148572   -.1614657
 post_mp_ada |  -.4741875   .4074018    -1.16   0.248    -1.284211    .3358363
 post_opo_mp |   .9580718   .5262889     1.82   0.072    -.0883316    2.004475
     hour_sq |  -.0011959   .0006226    -1.92   0.058    -.0024339     .000042
        hour |   .0284065   .0165927     1.71   0.091    -.0045842    .0613973
  gg_hashtag |  -.1844443   .1419194    -1.30   0.197     -.466618    .0977294
       gg_at |  -.4367681   .1076986    -4.06   0.000    -.6509017   -.2226345
    gg_reply |  -1.834198   .3060687    -5.99   0.000    -2.442745   -1.225652
       gg_rt |          0  (omitted)
     gg_http |    .269473   .0780622     3.45   0.001     .1142645    .4246815
       _cons |   3.565973   .2330048    15.30   0.000     3.102697    4.029248
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA4.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada post_mp_ada post_opo_mp) stats(N pFE
>  dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter enga
> gement: Interactions with MP status") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA4.tex)

. 
. 
. ***Table A5 **** 
. 
. bys post_ada government: sum rt_count  if timeWindow3_ada  & !gg_rt & mp

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 0, government = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    rt_count |      1,072    25.42071      47.887          0        416

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 0, government = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    rt_count |        401    40.02494    88.40868          0        651

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 1, government = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    rt_count |        956    23.02406     47.8683          0        484

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 1, government = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    rt_count |        498    47.17269    103.1248          0        755


. bys post_ada government: sum favcount  if timeWindow3_ada  & !gg_rt & mp

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 0, government = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    favcount |      1,072    100.9011    196.3055          0       1601

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 0, government = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    favcount |        401    179.0125    406.2812          0       2815

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 1, government = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    favcount |        956    112.7259    232.3976          0       1993

--------------------------------------------------------------------------------------------------------------------------------------
-> post_ada = 1, government = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    favcount |        498    229.8153    505.8287          0       3928


. 
. 
. **** Table A8  ****
. 
. eststo clear

. eststo, title("Engagment"): reghdfe log_engagement post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_a
> da & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      25.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7308
                                                  Adj R-squared   =     0.7222
                                                  Within R-sq.    =     0.4113
Number of clusters (userid)  =         82         Root MSE        =     1.1478

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ngA |  -1.489505   .8110439    -1.84   0.070    -3.103228    .1242181
     hour_sq |   .0009963   .0012977     0.77   0.445    -.0015857    .0035783
     hour_cu |  -.0000563    .000055    -1.02   0.309    -.0001658    .0000532
  gg_hashtag |  -.1457188   .1470822    -0.99   0.325    -.4383662    .1469286
       gg_at |  -.4809137   .1149705    -4.18   0.000     -.709669   -.2521584
    gg_reply |  -1.934317      .3025    -6.39   0.000    -2.536197   -1.332437
       gg_rt |          0  (omitted)
     gg_http |   .3239891   .0845927     3.83   0.000      .155676    .4923021
       _cons |   3.734351   .2001936    18.65   0.000     3.336029    4.132674
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_
> rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      16.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7187
                                                  Adj R-squared   =     0.7097
                                                  Within R-sq.    =     0.4407
Number of clusters (userid)  =         82         Root MSE        =     0.9382

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ngA |  -.9647816   .4711994    -2.05   0.044    -1.902321   -.0272426
     hour_sq |   .0003901   .0010112     0.39   0.701    -.0016219     .002402
     hour_cu |  -.0000361   .0000429    -0.84   0.402    -.0001216    .0000493
  gg_hashtag |  -.0642057   .1417426    -0.45   0.652    -.3462291    .2178176
       gg_at |  -.3502314   .1018989    -3.44   0.001    -.5529782   -.1474846
    gg_reply |  -1.717025   .2636777    -6.51   0.000    -2.241661   -1.192389
       gg_rt |          0  (omitted)
     gg_http |   .2947934   .0553127     5.33   0.000     .1847385    .4048482
       _cons |   2.409071   .1568572    15.36   0.000     2.096974    2.721168
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_rt
>  & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      21.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7250
                                                  Adj R-squared   =     0.7162
                                                  Within R-sq.    =     0.3892
Number of clusters (userid)  =         82         Root MSE        =     1.1244

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ngA |  -1.469393   .8152864    -1.80   0.075    -3.091558    .1527708
     hour_sq |   .0010676   .0012414     0.86   0.392    -.0014025    .0035377
     hour_cu |  -.0000565    .000052    -1.09   0.281      -.00016     .000047
  gg_hashtag |  -.1699931   .1420661    -1.20   0.235      -.45266    .1126738
       gg_at |    -.45525   .1089393    -4.18   0.000    -.6720051   -.2384949
    gg_reply |  -1.828193   .2919728    -6.26   0.000    -2.409127   -1.247259
       gg_rt |          0  (omitted)
     gg_http |   .2719404    .085179     3.19   0.002     .1024609    .4414198
       _cons |   3.554587   .2016799    17.62   0.000     3.153307    3.955866
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Engagment"): reghdfe log_engagement post_opo_ada post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if 
> timeWindow3_ada & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   8,     81) =      22.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7308
                                                  Adj R-squared   =     0.7222
                                                  Within R-sq.    =     0.4114
Number of clusters (userid)  =         82         Root MSE        =     1.1479

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.0360819   .2893729    -0.12   0.901    -.6118431    .5396794
    post_ngA |  -1.383757   1.301964    -1.06   0.291    -3.974256    1.206743
     hour_sq |   .0010061   .0012994     0.77   0.441    -.0015794    .0035916
     hour_cu |  -.0000567    .000055    -1.03   0.306    -.0001661    .0000527
  gg_hashtag |  -.1453748   .1468232    -0.99   0.325    -.4375069    .1467574
       gg_at |  -.4810958   .1146654    -4.20   0.000     -.709244   -.2529477
    gg_reply |  -1.934508   .3027494    -6.39   0.000    -2.536884   -1.332132
       gg_rt |          0  (omitted)
     gg_http |   .3240622    .084697     3.83   0.000     .1555417    .4925827
       _cons |   3.732197   .2049851    18.21   0.000     3.324341    4.140053
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindo
> w3_ada & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   8,     81) =      14.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7188
                                                  Adj R-squared   =     0.7097
                                                  Within R-sq.    =     0.4408
Number of clusters (userid)  =         82         Root MSE        =     0.9383

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1204785   .1841484    -0.65   0.515    -.4868761    .2459191
    post_ngA |  -.6116848    .809457    -0.76   0.452     -2.22225    .9988807
     hour_sq |   .0004229   .0010136     0.42   0.678    -.0015939    .0024396
     hour_cu |  -.0000375    .000043    -0.87   0.387    -.0001231    .0000482
  gg_hashtag |   -.063057   .1409679    -0.45   0.656     -.343539     .217425
       gg_at |  -.3508396   .1018159    -3.45   0.001    -.5534214   -.1482578
    gg_reply |  -1.717662   .2640525    -6.51   0.000    -2.243043    -1.19228
       gg_rt |          0  (omitted)
     gg_http |   .2950375   .0549305     5.37   0.000     .1857431    .4043319
       _cons |   2.401879    .158727    15.13   0.000     2.086062    2.717696
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada post_ngA hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3
> _ada & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   8,     81) =      19.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7250
                                                  Adj R-squared   =     0.7162
                                                  Within R-sq.    =     0.3892
Number of clusters (userid)  =         82         Root MSE        =     1.1246

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.0190101   .2926405    -0.06   0.948     -.601273    .5632527
    post_ngA |  -1.413679   1.298538    -1.09   0.280    -3.997361    1.170004
     hour_sq |   .0010728   .0012421     0.86   0.390    -.0013987    .0035442
     hour_cu |  -.0000567    .000052    -1.09   0.278    -.0001601    .0000467
  gg_hashtag |  -.1698118   .1418413    -1.20   0.235    -.4520315    .1124079
       gg_at |   -.455346   .1086293    -4.19   0.000    -.6714842   -.2392077
    gg_reply |  -1.828293   .2921827    -6.26   0.000    -2.409645   -1.246942
       gg_rt |          0  (omitted)
     gg_http |   .2719789   .0853169     3.19   0.002      .102225    .4417327
       _cons |   3.553452   .2059878    17.25   0.000     3.143601    3.963303
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA8.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada post_ngA) stats(N pFE dFE controls, 
> fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter engagement: Negativ
> e campaiging effect") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA8.tex)

. 
. 
. **** Tables A10****
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow2
> _ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   7,     79) =      11.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7532
                                                  Adj R-squared   =     0.7429
                                                  Within R-sq.    =     0.3749
Number of clusters (userid)  =         80         Root MSE        =     1.1272

                                (Std. Err. adjusted for 80 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |    -.21919   .1382869    -1.59   0.117    -.4944432    .0560633
     hour_sq |  -.0012225   .0006733    -1.82   0.073    -.0025628    .0001177
        hour |   .0307885   .0176074     1.75   0.084    -.0042582    .0658352
  gg_hashtag |  -.0330616   .1758081    -0.19   0.851    -.3829988    .3168757
       gg_at |   -.509995   .1417825    -3.60   0.001    -.7922061    -.227784
    gg_reply |  -1.803503   .4085903    -4.41   0.000    -2.616781    -.990224
       gg_rt |          0  (omitted)
     gg_http |   .2373018   .0695866     3.41   0.001     .0987931    .3758105
       _cons |   3.485271   .1760716    19.79   0.000     3.134809    3.835732
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow2_ada & !gg
> _rt, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   7,     79) =      13.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7391
                                                  Adj R-squared   =     0.7282
                                                  Within R-sq.    =     0.4159
Number of clusters (userid)  =         80         Root MSE        =     0.9195

                                (Std. Err. adjusted for 80 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.3258755   .1054776    -3.09   0.003    -.5358235   -.1159276
     hour_sq |   -.001402   .0006117    -2.29   0.025    -.0026195   -.0001845
        hour |   .0297057   .0155161     1.91   0.059    -.0011783    .0605898
  gg_hashtag |   .0503978   .1534167     0.33   0.743    -.2549705    .3557661
       gg_at |  -.4037475   .1181164    -3.42   0.001    -.6388523   -.1686426
    gg_reply |  -1.585772   .3500729    -4.53   0.000    -2.282574   -.8889689
       gg_rt |          0  (omitted)
     gg_http |   .2792677   .0531305     5.26   0.000     .1735141    .3850213
       _cons |   2.211248   .1427026    15.50   0.000     1.927206    2.495291
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow2_ada & !gg_r
> t, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   7,     79) =      10.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7502
                                                  Adj R-squared   =     0.7398
                                                  Within R-sq.    =     0.3507
Number of clusters (userid)  =         80         Root MSE        =     1.1025

                                (Std. Err. adjusted for 80 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |     -.1894   .1406418    -1.35   0.182    -.4693405    .0905406
     hour_sq |  -.0010961   .0006291    -1.74   0.085    -.0023483    .0001561
        hour |   .0287783   .0166716     1.73   0.088    -.0044057    .0619624
  gg_hashtag |   -.063245   .1730639    -0.37   0.716    -.4077202    .2812302
       gg_at |  -.4737258   .1330269    -3.56   0.001    -.7385091   -.2089425
    gg_reply |  -1.696832   .3914257    -4.34   0.000    -2.475945   -.9177185
       gg_rt |          0  (omitted)
     gg_http |   .1868363   .0690639     2.71   0.008     .0493681    .3243044
       _cons |   3.307826   .1763096    18.76   0.000     2.956891    3.658762
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_repl
> y gg_rt gg_http if timeWindow2_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   9,     79) =      30.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7603
                                                  Adj R-squared   =     0.7501
                                                  Within R-sq.    =     0.3930
Number of clusters (userid)  =         80         Root MSE        =     1.1113

                                    (Std. Err. adjusted for 80 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
  log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0397935   .1556675    -0.26   0.799    -.3496418    .2700549
    mentionOther |   .7251315    .136852     5.30   0.000     .4527344    .9975285
post_mention_ada |  -.4511083   .2741194    -1.65   0.104    -.9967293    .0945127
         hour_sq |  -.0010727   .0006584    -1.63   0.107    -.0023833    .0002378
            hour |   .0271987   .0174222     1.56   0.122    -.0074793    .0618768
      gg_hashtag |  -.0673772   .1624378    -0.41   0.679    -.3907016    .2559472
           gg_at |  -.5195143    .132078    -3.93   0.000    -.7824089   -.2566196
        gg_reply |  -1.703268   .3944029    -4.32   0.000    -2.488307   -.9182285
           gg_rt |          0  (omitted)
         gg_http |   .2274055   .0677593     3.36   0.001      .092534     .362277
           _cons |   3.336987   .1782865    18.72   0.000     2.982117    3.691858
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg
> _http if timeWindow2_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   9,     79) =      28.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7489
                                                  Adj R-squared   =     0.7382
                                                  Within R-sq.    =     0.4378
Number of clusters (userid)  =         80         Root MSE        =     0.9025

                                    (Std. Err. adjusted for 80 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
          log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.1456617   .1265892    -1.15   0.253    -.3976311    .1063077
    mentionOther |   .6967474   .1245876     5.59   0.000      .448762    .9447329
post_mention_ada |   -.502476   .2466252    -2.04   0.045    -.9933712   -.0115808
         hour_sq |  -.0012654   .0005857    -2.16   0.034    -.0024311   -.0000997
            hour |   .0265122   .0150296     1.76   0.082    -.0034034    .0564279
      gg_hashtag |   .0169209   .1401642     0.12   0.904     -.262069    .2959107
           gg_at |  -.4107808   .1103203    -3.72   0.000    -.6303679   -.1911936
        gg_reply |   -1.49169   .3338341    -4.47   0.000    -2.156171   -.8272101
           gg_rt |          0  (omitted)
         gg_http |   .2716223   .0534746     5.08   0.000     .1651837    .3780608
           _cons |   2.069524   .1452612    14.25   0.000     1.780389    2.358659
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_h
> ttp if timeWindow2_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 7 singleton observations)
(MWFE estimator converged in 8 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      2,652
Absorbing 2 HDFE groups                           F(   9,     79) =      26.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7570
                                                  Adj R-squared   =     0.7467
                                                  Within R-sq.    =     0.3685
Number of clusters (userid)  =         80         Root MSE        =     1.0877

                                    (Std. Err. adjusted for 80 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
         log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0189907   .1573094    -0.12   0.904    -.3321071    .2941258
    mentionOther |   .6900761   .1343917     5.13   0.000     .4225761     .957576
post_mention_ada |  -.4265308   .2658916    -1.60   0.113    -.9557748    .1027132
         hour_sq |  -.0009533   .0006188    -1.54   0.127     -.002185    .0002785
            hour |   .0253519     .01656     1.53   0.130    -.0076099    .0583138
      gg_hashtag |  -.0958814   .1604098    -0.60   0.552    -.4151692    .2234064
           gg_at |  -.4828696   .1236384    -3.91   0.000    -.7289657   -.2367735
        gg_reply |  -1.601353   .3778717    -4.24   0.000    -2.353488   -.8492187
           gg_rt |          0  (omitted)
         gg_http |   .1773436   .0669062     2.65   0.010     .0441702     .310517
           _cons |   3.166681   .1785592    17.73   0.000     2.811268    3.522094
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        80          80           0    *|
       date2 |        20           0          20     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA10.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada mentionOther post_mention_ada) stat
> s(N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitt
> er engagement (10-day window)") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA10.tex)

. 
. 
. **** Tables A11 ****
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow4
> _ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   7,     87) =      15.67
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7393
                                                  Adj R-squared   =     0.7326
                                                  Within R-sq.    =     0.4106
Number of clusters (userid)  =         88         Root MSE        =     1.1472

                                (Std. Err. adjusted for 88 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1446778   .1702803    -0.85   0.398    -.4831284    .1937728
     hour_sq |  -.0012926   .0005964    -2.17   0.033     -.002478   -.0001071
        hour |   .0267362   .0161963     1.65   0.102    -.0054557    .0589281
  gg_hashtag |  -.1488584   .1180528    -1.26   0.211    -.3835012    .0857843
       gg_at |   -.401656   .0891355    -4.51   0.000    -.5788224   -.2244896
    gg_reply |  -1.967311    .283659    -6.94   0.000    -2.531114   -1.403508
       gg_rt |          0  (omitted)
     gg_http |   .3087152   .0723437     4.27   0.000     .1649242    .4525062
       _cons |   3.634574   .1689296    21.52   0.000     3.298808    3.970339
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow4_ada & !gg
> _rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   7,     87) =      13.02
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7272
                                                  Adj R-squared   =     0.7202
                                                  Within R-sq.    =     0.4340
Number of clusters (userid)  =         88         Root MSE        =     0.9533

                                (Std. Err. adjusted for 88 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1285239   .1017023    -1.26   0.210    -.3306682    .0736205
     hour_sq |  -.0010466   .0005394    -1.94   0.056    -.0021187    .0000255
        hour |   .0179106   .0152813     1.17   0.244    -.0124626    .0482839
  gg_hashtag |  -.0426158   .1175927    -0.36   0.718    -.2763439    .1911124
       gg_at |   -.287943    .086792    -3.32   0.001    -.4604516   -.1154345
    gg_reply |  -1.744765   .2490779    -7.00   0.000    -2.239834   -1.249695
       gg_rt |          0  (omitted)
     gg_http |   .2778348   .0601082     4.62   0.000     .1583632    .3973064
       _cons |   2.321928    .145499    15.96   0.000     2.032733    2.611123
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow4_ada & !gg_r
> t, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   7,     87) =      14.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7338
                                                  Adj R-squared   =     0.7270
                                                  Within R-sq.    =     0.3879
Number of clusters (userid)  =         88         Root MSE        =     1.1245

                                (Std. Err. adjusted for 88 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1362843   .1732088    -0.79   0.434    -.4805554    .2079869
     hour_sq |  -.0011676   .0005665    -2.06   0.042    -.0022936   -.0000416
        hour |   .0248684   .0154289     1.61   0.111    -.0057983     .055535
  gg_hashtag |  -.1768126   .1142858    -1.55   0.125    -.4039681    .0503428
       gg_at |  -.3794693   .0843874    -4.50   0.000    -.5471984   -.2117403
    gg_reply |  -1.862084   .2743402    -6.79   0.000    -2.407365   -1.316803
       gg_rt |          0  (omitted)
     gg_http |   .2579434   .0718292     3.59   0.001     .1151751    .4007117
       _cons |   3.473923   .1683104    20.64   0.000     3.139388    3.808458
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_repl
> y gg_rt gg_http if timeWindow4_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   9,     87) =      28.55
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7495
                                                  Adj R-squared   =     0.7430
                                                  Within R-sq.    =     0.4337
Number of clusters (userid)  =         88         Root MSE        =     1.1246

                                    (Std. Err. adjusted for 88 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
  log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0125007   .1832437    -0.07   0.946    -.3767175    .3517161
    mentionOther |   .8632451   .1220243     7.07   0.000     .6207086    1.105782
post_mention_ada |  -.3826666   .2193791    -1.74   0.085    -.8187063    .0533731
         hour_sq |  -.0011221   .0005601    -2.00   0.048    -.0022353   -8.87e-06
            hour |   .0231566   .0153811     1.51   0.136    -.0074151    .0537283
      gg_hashtag |   -.185742   .1078521    -1.72   0.089    -.4001097    .0286258
           gg_at |  -.4190817   .0827254    -5.07   0.000    -.5835073   -.2546561
        gg_reply |  -1.853514   .2662188    -6.96   0.000    -2.382653   -1.324375
           gg_rt |          0  (omitted)
         gg_http |   .2922129   .0697864     4.19   0.000     .1535049     .430921
           _cons |   3.471999   .1606062    21.62   0.000     3.152777    3.791222
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg
> _http if timeWindow4_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   9,     87) =      31.06
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7407
                                                  Adj R-squared   =     0.7339
                                                  Within R-sq.    =     0.4621
Number of clusters (userid)  =         88         Root MSE        =     0.9295

                                    (Std. Err. adjusted for 88 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
          log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0027616   .1229678    -0.02   0.982    -.2471734    .2416502
    mentionOther |   .8109129   .1142073     7.10   0.000     .5839135    1.037912
post_mention_ada |  -.3673475   .2152689    -1.71   0.091    -.7952178    .0605227
         hour_sq |  -.0008866   .0004952    -1.79   0.077    -.0018708    .0000976
            hour |   .0145393   .0144627     1.01   0.318    -.0142069    .0432855
      gg_hashtag |  -.0770834   .1068754    -0.72   0.473    -.2895098     .135343
           gg_at |  -.3042096   .0809434    -3.76   0.000    -.4650933   -.1433259
        gg_reply |  -1.638193   .2310548    -7.09   0.000     -2.09744   -1.178947
           gg_rt |          0  (omitted)
         gg_http |   .2626245   .0588318     4.46   0.000     .1456899    .3795591
           _cons |   2.169617    .137465    15.78   0.000      1.89639    2.442843
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_h
> ttp if timeWindow4_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      5,331
Absorbing 2 HDFE groups                           F(   9,     87) =      25.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7439
                                                  Adj R-squared   =     0.7372
                                                  Within R-sq.    =     0.4110
Number of clusters (userid)  =         88         Root MSE        =     1.1033

                                    (Std. Err. adjusted for 88 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
         log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0110844   .1853872    -0.06   0.952    -.3795614    .3573927
    mentionOther |   .8256767   .1193834     6.92   0.000     .5883893    1.062964
post_mention_ada |  -.3599739   .2118916    -1.70   0.093    -.7811314    .0611837
         hour_sq |  -.0010045    .000534    -1.88   0.063    -.0020659     .000057
            hour |   .0214512   .0146497     1.46   0.147    -.0076668    .0505691
      gg_hashtag |  -.2122289   .1047855    -2.03   0.046    -.4205015   -.0039563
           gg_at |  -.3962154   .0780176    -5.08   0.000    -.5512839    -.241147
        gg_reply |  -1.752989   .2574012    -6.81   0.000    -2.264602   -1.241376
           gg_rt |          0  (omitted)
         gg_http |   .2419358   .0692512     3.49   0.001     .1042916    .3795799
           _cons |   3.318112   .1601188    20.72   0.000     2.999859    3.636366
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        88          88           0    *|
       date2 |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA11.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada mentionOther post_mention_ada) stat
> s(N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitt
> er engagement (20-day window)") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA11.tex)

. 
. 
. **** Tables A12 ****
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow5
> _ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   7,     91) =      15.19
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7299
                                                  Adj R-squared   =     0.7246
                                                  Within R-sq.    =     0.3909
Number of clusters (userid)  =         92         Root MSE        =     1.1701

                                (Std. Err. adjusted for 92 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1699622   .1515052    -1.12   0.265    -.4709088    .1309843
     hour_sq |  -.0004153    .000594    -0.70   0.486    -.0015952    .0007646
        hour |    .004632   .0156626     0.30   0.768    -.0264798    .0357437
  gg_hashtag |  -.0808391   .1235623    -0.65   0.515    -.3262804    .1646023
       gg_at |  -.4027456   .0953437    -4.22   0.000    -.5921342   -.2133569
    gg_reply |  -1.905323   .2817932    -6.76   0.000    -2.465071   -1.345575
       gg_rt |          0  (omitted)
     gg_http |   .3692869   .0739818     4.99   0.000     .2223312    .5162426
       _cons |   3.733053   .1587179    23.52   0.000      3.41778    4.048327
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow5_ada & !gg
> _rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   7,     91) =      12.94
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7119
                                                  Adj R-squared   =     0.7062
                                                  Within R-sq.    =     0.4173
Number of clusters (userid)  =         92         Root MSE        =     0.9898

                                (Std. Err. adjusted for 92 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1169358   .0926064    -1.26   0.210    -.3008869    .0670154
     hour_sq |  -.0005065   .0004933    -1.03   0.307    -.0014864    .0004734
        hour |   .0053103   .0139485     0.38   0.704    -.0223966    .0330172
  gg_hashtag |  -.0035382    .123003    -0.03   0.977    -.2478685    .2407921
       gg_at |  -.3122763   .0934619    -3.34   0.001    -.4979269   -.1266258
    gg_reply |  -1.714565   .2604955    -6.58   0.000    -2.232007   -1.197123
       gg_rt |          0  (omitted)
     gg_http |   .3511855   .0559953     6.27   0.000     .2399577    .4624132
       _cons |   2.352846   .1503305    15.65   0.000     2.054233    2.651459
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow5_ada & !gg_r
> t, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   7,     91) =      13.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7262
                                                  Adj R-squared   =     0.7208
                                                  Within R-sq.    =     0.3704
Number of clusters (userid)  =         92         Root MSE        =     1.1442

                                (Std. Err. adjusted for 92 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.1687306   .1535579    -1.10   0.275    -.4737546    .1362934
     hour_sq |  -.0002905   .0005584    -0.52   0.604    -.0013996    .0008187
        hour |   .0023711   .0148633     0.16   0.874    -.0271532    .0318953
  gg_hashtag |  -.1084376   .1195635    -0.91   0.367    -.3459358    .1290605
       gg_at |  -.3819586   .0918819    -4.16   0.000    -.5644709   -.1994464
    gg_reply |  -1.804052   .2740674    -6.58   0.000    -2.348453    -1.25965
       gg_rt |          0  (omitted)
     gg_http |   .3167013   .0741586     4.27   0.000     .1693943    .4640083
       _cons |   3.581429   .1580954    22.65   0.000     3.267392    3.895466
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_repl
> y gg_rt gg_http if timeWindow5_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   9,     91) =      22.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7405
                                                  Adj R-squared   =     0.7353
                                                  Within R-sq.    =     0.4148
Number of clusters (userid)  =         92         Root MSE        =     1.1471

                                    (Std. Err. adjusted for 92 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
  log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0533456   .1584371    -0.34   0.737    -.3680615    .2613703
    mentionOther |   .8495714   .1186669     7.16   0.000     .6138541    1.085289
post_mention_ada |  -.3255229   .1769041    -1.84   0.069    -.6769211    .0258753
         hour_sq |  -.0003285   .0005532    -0.59   0.554    -.0014275    .0007704
            hour |   .0034148   .0146399     0.23   0.816    -.0256656    .0324953
      gg_hashtag |  -.1185815   .1130129    -1.05   0.297    -.3430678    .1059048
           gg_at |  -.4264807   .0886116    -4.81   0.000    -.6024967   -.2504647
        gg_reply |  -1.788719   .2598635    -6.88   0.000    -2.304906   -1.272532
           gg_rt |          0  (omitted)
         gg_http |   .3510013   .0704799     4.98   0.000     .2110017    .4910009
           _cons |   3.562165   .1477806    24.10   0.000     3.268617    3.855713
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est4 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg
> _http if timeWindow5_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   9,     91) =      24.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7256
                                                  Adj R-squared   =     0.7201
                                                  Within R-sq.    =     0.4451
Number of clusters (userid)  =         92         Root MSE        =     0.9660

                                    (Std. Err. adjusted for 92 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
          log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0160147   .1092643    -0.15   0.884    -.2330548    .2010253
    mentionOther |   .7764556   .1102651     7.04   0.000     .5574275    .9954836
post_mention_ada |  -.2718199   .1874897    -1.45   0.151    -.6442452    .1006054
         hour_sq |  -.0004302   .0004579    -0.94   0.350    -.0013397    .0004794
            hour |   .0043431   .0131871     0.33   0.743    -.0218516    .0305377
      gg_hashtag |  -.0387122     .11325    -0.34   0.733    -.2636694    .1862451
           gg_at |  -.3341793   .0874268    -3.82   0.000    -.5078419   -.1605168
        gg_reply |  -1.606461   .2382263    -6.74   0.000    -2.079668   -1.133253
           gg_rt |          0  (omitted)
         gg_http |   .3339568   .0538612     6.20   0.000     .2269682    .4409454
           _cons |   2.193874   .1386073    15.83   0.000     1.918547      2.4692
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est5 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada mentionOther post_mention_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_h
> ttp if timeWindow5_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      8,130
Absorbing 2 HDFE groups                           F(   9,     91) =      20.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7366
                                                  Adj R-squared   =     0.7313
                                                  Within R-sq.    =     0.3943
Number of clusters (userid)  =         92         Root MSE        =     1.1225

                                    (Std. Err. adjusted for 92 clusters in userid)
----------------------------------------------------------------------------------
                 |               Robust
         log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
    post_opo_ada |  -.0573944   .1599013    -0.36   0.720    -.3750186    .2602298
    mentionOther |   .8147786   .1176821     6.92   0.000     .5810176     1.04854
post_mention_ada |  -.3099011   .1714946    -1.81   0.074    -.6505541    .0307519
         hour_sq |  -.0002075    .000522    -0.40   0.692    -.0012444    .0008294
            hour |   .0012167   .0139037     0.09   0.930    -.0264013    .0288348
      gg_hashtag |   -.144695   .1096306    -1.32   0.190    -.3624626    .0730727
           gg_at |  -.4047405   .0852936    -4.75   0.000    -.5741657   -.2353153
        gg_reply |  -1.692086   .2528472    -6.69   0.000    -2.194336   -1.189836
           gg_rt |          0  (omitted)
         gg_http |   .2991185   .0707454     4.23   0.000     .1585915    .4396454
           _cons |    3.41729   .1476343    23.15   0.000     3.124032    3.710547
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        92          92           0    *|
       date2 |        60           0          60     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est6 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA12.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada mentionOther post_mention_ada) stat
> s(N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitt
> er engagement (30-day window)") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA12.tex)

. 
. 
. 
. ***Table A13 ****
. 
. label variable opo_ada "Mention Adamowicz x opposition"

. label variable gg_adamowicz "Mention Adamowicz"

. 
. eststo clear

. 
. eststo, title("Engagement"): reghdfe log_engagement gg_adamowicz opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if 
> timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   8,     85) =      19.30
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7372
                                                  Adj R-squared   =     0.7288
                                                  Within R-sq.    =     0.4055
Number of clusters (userid)  =         86         Root MSE        =     1.1464

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gg_adamowicz |   .5144261   .2102763     2.45   0.016     .0963405    .9325118
     opo_ada |  -.1958625    .275894    -0.71   0.480    -.7444137    .3526887
     hour_sq |   .0014839   .0012949     1.15   0.255    -.0010907    .0040585
     hour_cu |  -.0000768   .0000553    -1.39   0.168    -.0001867    .0000331
  gg_hashtag |   -.167116   .1497955    -1.12   0.268    -.4649497    .1307176
       gg_at |  -.4575903   .1140666    -4.01   0.000    -.6843852   -.2307953
    gg_reply |  -1.937798   .3156938    -6.14   0.000    -2.565482   -1.310114
       gg_rt |          0  (omitted)
     gg_http |   .3222467   .0786224     4.10   0.000     .1659244     .478569
       _cons |   3.580943   .1499204    23.89   0.000     3.282861    3.879025
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt gg_adamowicz opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow
> 3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   8,     85) =      14.59
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7251
                                                  Adj R-squared   =     0.7164
                                                  Within R-sq.    =     0.4358
Number of clusters (userid)  =         86         Root MSE        =     0.9382

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gg_adamowicz |   .3617156   .2288594     1.58   0.118    -.0933182    .8167493
     opo_ada |  -.3463845   .2621228    -1.32   0.190    -.8675549    .1747859
     hour_sq |   .0007639    .001038     0.74   0.464    -.0012999    .0028276
     hour_cu |  -.0000518   .0000443    -1.17   0.246    -.0001398    .0000363
  gg_hashtag |  -.0817296   .1399886    -0.58   0.561    -.3600644    .1966053
       gg_at |  -.3399562    .100035    -3.40   0.001    -.5388526   -.1410598
    gg_reply |  -1.719474   .2695346    -6.38   0.000    -2.255381   -1.183567
       gg_rt |          0  (omitted)
     gg_http |   .2957583   .0534345     5.53   0.000     .1895161    .4020004
       _cons |   2.309207   .1360145    16.98   0.000     2.038774    2.579641
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav gg_adamowicz opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_
> ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   8,     85) =      16.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7320
                                                  Adj R-squared   =     0.7234
                                                  Within R-sq.    =     0.3833
Number of clusters (userid)  =         86         Root MSE        =     1.1238

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
gg_adamowicz |   .5191007   .2158973     2.40   0.018     .0898389    .9483624
     opo_ada |  -.1922242    .278025    -0.69   0.491    -.7450124     .360564
     hour_sq |   .0015468   .0012405     1.25   0.216    -.0009198    .0040133
     hour_cu |  -.0000766   .0000524    -1.46   0.147    -.0001808    .0000276
  gg_hashtag |   -.191892   .1453291    -1.32   0.190    -.4808452    .0970612
       gg_at |  -.4320798   .1078575    -4.01   0.000    -.6465294   -.2176303
    gg_reply |  -1.831856   .3054822    -6.00   0.000    -2.439237   -1.224476
       gg_rt |          0  (omitted)
     gg_http |   .2709617   .0789411     3.43   0.001     .1140058    .4279177
       _cons |     3.4036   .1504368    22.62   0.000     3.104492    3.702709
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. 
. esttab using "${PathTab}TableA13.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(gg_adamowicz opo_ada) stats(N controls, fmt(0) l
> abels("N" "Controls")) label nodepvar mtitles title("Adamowicz Mentions and Twitter engagement") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA13.tex)

. 
. 
. ***Table A14 **** 
. 
. eststo clear

. * eststo, title("Engagement"): reg log_engagement post_ada post_opo_ada if timeWindow3_ada & !gg_rt & !gg_adamowicz, cl(userid)
. * estadd local dFE = "No"
. * estadd local pFE = "No"
. * estadd local controls = "No"
. * eststo, title("Retweets"): reg log_rt post_ada post_opo_ada if timeWindow3_ada & !gg_rt & !gg_adamowicz, cl(userid)
. * estadd local dFE = "No"
. * estadd local pFE = "No"
. * estadd local controls = "No"
. * eststo, title("Likes"): reg log_fav post_ada post_opo_ada if timeWindow3_ada & !gg_rt & !gg_adamowicz, cl(userid)
. * estadd local dFE = "No"
. * estadd local pFE = "No"
. * estadd local controls = "No"
. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3
> _ada & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      20.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7304
                                                  Adj R-squared   =     0.7218
                                                  Within R-sq.    =     0.4105
Number of clusters (userid)  =         82         Root MSE        =     1.1487

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.3476259   .1912168    -1.82   0.073    -.7280872    .0328355
     hour_sq |  -.0012948   .0006466    -2.00   0.049    -.0025814   -8.26e-06
        hour |   .0277309   .0167087     1.66   0.101    -.0055143     .060976
  gg_hashtag |  -.1398211   .1460973    -0.96   0.341    -.4305088    .1508667
       gg_at |  -.4782271   .1154734    -4.14   0.000    -.7079831   -.2484712
    gg_reply |  -1.939234   .3039019    -6.38   0.000    -2.543903   -1.334564
       gg_rt |          0  (omitted)
     gg_http |   .3266078   .0848327     3.85   0.000     .1578174    .4953983
       _cons |   3.580215   .1796653    19.93   0.000     3.222737    3.937692
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg
> _rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      16.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7188
                                                  Adj R-squared   =     0.7098
                                                  Within R-sq.    =     0.4408
Number of clusters (userid)  =         82         Root MSE        =     0.9381

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.2581503   .1108865    -2.33   0.022    -.4787797   -.0375209
     hour_sq |  -.0013277   .0005246    -2.53   0.013    -.0023714    -.000284
        hour |   .0248916   .0141024     1.77   0.081    -.0031678     .052951
  gg_hashtag |  -.0613386   .1408654    -0.44   0.664    -.3416167    .2189394
       gg_at |  -.3500501   .1020911    -3.43   0.001    -.5531795   -.1469208
    gg_reply |  -1.719151   .2639404    -6.51   0.000    -2.244309   -1.193992
       gg_rt |          0  (omitted)
     gg_http |    .296082   .0544253     5.44   0.000     .1877929    .4043712
       _cons |   2.273389   .1491722    15.24   0.000     1.976583    2.570195
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_r
> t & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 2 HDFE groups                           F(   7,     81) =      18.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7245
                                                  Adj R-squared   =     0.7157
                                                  Within R-sq.    =     0.3882
Number of clusters (userid)  =         82         Root MSE        =     1.1254

                                (Std. Err. adjusted for 82 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.3372787    .194437    -1.73   0.087    -.7241475      .04959
     hour_sq |  -.0011664   .0006182    -1.89   0.063    -.0023964    .0000637
        hour |   .0259372   .0160183     1.62   0.109    -.0059341    .0578086
  gg_hashtag |  -.1639144    .141214    -1.16   0.249    -.4448861    .1170572
       gg_at |  -.4522989    .109438    -4.13   0.000    -.6700461   -.2345517
    gg_reply |  -1.833239   .2935283    -6.25   0.000    -2.417268    -1.24921
       gg_rt |          0  (omitted)
     gg_http |   .2746182    .085531     3.21   0.002     .1044384    .4447981
       _cons |   3.411412   .1795977    18.99   0.000     3.054068    3.768755
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82          82           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA14.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada) stats(N pFE dFE controls, fmt(0) l
> abels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter engagement (tweets about Ad
> amowicz excluded)") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA14.tex)

. 
. 
. ***Table A15 ****
. 
. 
. eststo clear

. 
. eststo, title("Engagment"): reghdfe log_engagement post_pop hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_a
> da & !gg_rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,178
Absorbing 2 HDFE groups                           F(   7,     80) =      35.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7167
                                                  Adj R-squared   =     0.7058
                                                  Within R-sq.    =     0.4689
Number of clusters (userid)  =         81         Root MSE        =     1.1678

                                (Std. Err. adjusted for 81 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_pop |  -.0543172   .0603208    -0.90   0.371    -.1743594    .0657251
     hour_sq |   .0021547   .0014237     1.51   0.134    -.0006786     .004988
     hour_cu |  -.0001105   .0000575    -1.92   0.058    -.0002248    3.82e-06
  gg_hashtag |  -.2107153   .1374022    -1.53   0.129    -.4841544    .0627239
       gg_at |  -.4094027   .1072717    -3.82   0.000    -.6228802   -.1959253
    gg_reply |  -2.233589    .181273   -12.32   0.000    -2.594334   -1.872844
       gg_rt |          0  (omitted)
     gg_http |    .384978    .086699     4.44   0.000     .2124415    .5575145
       _cons |   3.849014   .2095811    18.37   0.000     3.431934    4.266093
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        81          81           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_pop hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_
> rt & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,178
Absorbing 2 HDFE groups                           F(   7,     80) =      35.68
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7109
                                                  Adj R-squared   =     0.6999
                                                  Within R-sq.    =     0.4865
Number of clusters (userid)  =         81         Root MSE        =     0.9753

                                (Std. Err. adjusted for 81 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_pop |   -.004909   .0367523    -0.13   0.894    -.0780484    .0682303
     hour_sq |   .0011992   .0012017     1.00   0.321    -.0011923    .0035907
     hour_cu |  -.0000726   .0000499    -1.46   0.149    -.0001718    .0000266
  gg_hashtag |  -.1241148    .132578    -0.94   0.352    -.3879534    .1397238
       gg_at |  -.3038623   .1055306    -2.88   0.005    -.5138749   -.0938497
    gg_reply |  -1.989572   .1523752   -13.06   0.000    -2.292808   -1.686335
       gg_rt |          0  (omitted)
     gg_http |   .2871879    .073712     3.90   0.000     .1404964    .4338794
       _cons |   2.461336   .1485705    16.57   0.000     2.165671       2.757
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        81          81           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_pop hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_rt
>  & !gg_adamowicz, cl(userid) abs(userid date2)
(dropped 4 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,178
Absorbing 2 HDFE groups                           F(   7,     80) =      31.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7084
                                                  Adj R-squared   =     0.6973
                                                  Within R-sq.    =     0.4447
Number of clusters (userid)  =         81         Root MSE        =     1.1517

                                (Std. Err. adjusted for 81 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_pop |  -.0612551   .0610936    -1.00   0.319    -.1828353    .0603251
     hour_sq |   .0020663   .0014162     1.46   0.148     -.000752    .0048847
     hour_cu |  -.0001036    .000057    -1.82   0.073    -.0002171    9.88e-06
  gg_hashtag |  -.2327247   .1329632    -1.75   0.084    -.4973299    .0318805
       gg_at |   -.390697   .1025643    -3.81   0.000    -.5948064   -.1865876
    gg_reply |  -2.114374   .1785955   -11.84   0.000     -2.46979   -1.758957
       gg_rt |          0  (omitted)
     gg_http |   .3365034   .0855857     3.93   0.000     .1661824    .5068244
       _cons |   3.683028   .2117428    17.39   0.000     3.261646    4.104409
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        81          81           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. 
. esttab using "${PathTab}TableA15.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_pop) stats(N controls, fmt(0) labels("N" "C
> ontrols")) label nodepvar mtitles title("Violent attack and Twitter engagement (tweets about Adamowicz excluded)") replace nonotes p
> ostfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA15.tex)

. 
. 
. ***Table A16 ****
. 
. label variable log_n_tweets "Log tweets"

. eststo clear

. eststo, title("Engagment"): reghdfe log_engagement log_n_tweets, abs(userid date2)
(dropped 291 singleton observations)
(MWFE estimator converged in 11 iterations)

HDFE Linear regression                            Number of obs   =    321,922
Absorbing 2 HDFE groups                           F(   1, 319281) =    4639.65
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3579
                                                  Adj R-squared   =     0.3526
                                                  Within R-sq.    =     0.0143
                                                  Root MSE        =     1.4018

------------------------------------------------------------------------------
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
log_n_tweets |  -.2280574   .0033481   -68.11   0.000    -.2346196   -.2214951
       _cons |   3.730537   .0085745   435.07   0.000     3.713732    3.747343
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |       109           0         109     |
       date2 |      2532           1        2531     |
-----------------------------------------------------+
(est1 stored)

. estadd local controls = "No"

added macro:
           e(controls) : "No"

. esttab using "${PathTab}TableA16.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(log_n_tweets) stats(N controls, fmt(0) labels("N
> " "Controls")) label nodepvar mtitles title("Twitter production and Twitter engagement") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA16.tex)

. 
. 
. **** Table A17 ****
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWind
> ow3_ada & !gg_rt & popularity<3.05073, cl(userid) abs(userid date2)
(dropped 1 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   7,     39) =      12.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5831
                                                  Adj R-squared   =     0.5654
                                                  Within R-sq.    =     0.2401
Number of clusters (userid)  =         40         Root MSE        =     1.0439

                                (Std. Err. adjusted for 40 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.5167198   .2287749    -2.26   0.030    -.9794607   -.0539788
     hour_sq |  -.0005721   .0011333    -0.50   0.617    -.0028644    .0017202
     hour_cu |   7.72e-06    .000055     0.14   0.889    -.0001035    .0001189
  gg_hashtag |   .0808244   .1771052     0.46   0.651    -.2774047    .4390535
       gg_at |  -.3861404   .1249586    -3.09   0.004     -.638893   -.1333879
    gg_reply |  -1.196433    .446296    -2.68   0.011    -2.099152   -.2937143
       gg_rt |          0  (omitted)
     gg_http |   .2668374   .1293818     2.06   0.046      .005138    .5285368
       _cons |   2.550212   .1760236    14.49   0.000     2.194171    2.906253
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        40          40           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & 
> !gg_rt & popularity<3.05073, cl(userid) abs(userid date2)
(dropped 1 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   7,     39) =      11.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5510
                                                  Adj R-squared   =     0.5319
                                                  Within R-sq.    =     0.2907
Number of clusters (userid)  =         40         Root MSE        =     0.7850

                                (Std. Err. adjusted for 40 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.4029054   .1367771    -2.95   0.005    -.6795633   -.1262476
     hour_sq |  -.0006639   .0010545    -0.63   0.533    -.0027968    .0014691
     hour_cu |   8.65e-06   .0000493     0.18   0.862    -.0000911    .0001084
  gg_hashtag |   .0747574    .156038     0.48   0.635    -.2408592    .3903741
       gg_at |  -.2510997   .0830914    -3.02   0.004    -.4191679   -.0830314
    gg_reply |  -1.051404   .3710209    -2.83   0.007    -1.801865   -.3009432
       gg_rt |          0  (omitted)
     gg_http |   .2808027   .0774533     3.63   0.001     .1241386    .4374669
       _cons |   1.459776    .173909     8.39   0.000     1.108012    1.811541
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        40          40           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !g
> g_rt & popularity<3.05073, cl(userid) abs(userid date2)
(dropped 1 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,868
Absorbing 2 HDFE groups                           F(   7,     39) =      10.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5740
                                                  Adj R-squared   =     0.5559
                                                  Within R-sq.    =     0.2182
Number of clusters (userid)  =         40         Root MSE        =     1.0074

                                (Std. Err. adjusted for 40 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -.5042469   .2362096    -2.13   0.039    -.9820258   -.0264679
     hour_sq |  -.0004804   .0009894    -0.49   0.630    -.0024817     .001521
     hour_cu |   7.62e-06   .0000471     0.16   0.872    -.0000876    .0001029
  gg_hashtag |   .0619455   .1632688     0.38   0.706    -.2682969    .3921878
       gg_at |  -.3543238   .1183017    -3.00   0.005    -.5936117    -.115036
    gg_reply |  -1.095138   .4214975    -2.60   0.013    -1.947697   -.2425792
       gg_rt |          0  (omitted)
     gg_http |   .2228843   .1307607     1.70   0.096    -.0416042    .4873727
       _cons |   2.356836    .172267    13.68   0.000     2.008393    2.705279
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        40          40           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. 
. esttab using "${PathTab}TableA17.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada) stats(N controls, fmt(0) labels("N
> " "Controls")) label nodepvar mtitles title("Violent attack and Twitter engagement (50\% most popular)") replace nonotes postfoot(" 
> ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA17.tex)

. 
. 
. 
. 
. 
. ***Table A21 **** 
. 
. gen totaleng = favcount + rt_count
(1 missing value generated)

. eststo clear

. eststo, title("Engagement"): reghdfe totaleng post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada &
>  !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =       4.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0002
                                                  R-squared       =     0.6548
                                                  Adj R-squared   =     0.6438
                                                  Within R-sq.    =     0.0500
Number of clusters (userid)  =         86         Root MSE        =   353.6299

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
    totaleng |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -114.0685   62.21662    -1.83   0.070    -237.7718    9.634802
     hour_sq |   .0677562   .1409345     0.48   0.632    -.2124594    .3479718
        hour |  -1.266683   3.870678    -0.33   0.744    -8.962627    6.429262
  gg_hashtag |  -104.7323   39.55404    -2.65   0.010    -183.3763   -26.08826
       gg_at |  -33.00136   21.24859    -1.55   0.124    -75.24925    9.246523
    gg_reply |  -181.4704   42.85899    -4.23   0.000    -266.6856    -96.2553
       gg_rt |          0  (omitted)
     gg_http |  -5.585382   16.88316    -0.33   0.742    -39.15363    27.98287
       _cons |   307.1295   48.04303     6.39   0.000     211.6071    402.6519
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe rt_count post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !
> gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =       5.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.5594
                                                  Adj R-squared   =     0.5455
                                                  Within R-sq.    =     0.0665
Number of clusters (userid)  =         86         Root MSE        =    57.3674

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
    rt_count |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -17.45038   9.429679    -1.85   0.068     -36.1991    1.298352
     hour_sq |  -.0070489    .022832    -0.31   0.758     -.052445    .0383471
        hour |   .0035304    .693534     0.01   0.996    -1.375401    1.382462
  gg_hashtag |  -13.31942   6.337201    -2.10   0.039    -25.91948   -.7193701
       gg_at |  -2.281181   3.856829    -0.59   0.556    -9.949591     5.38723
    gg_reply |  -37.31958   8.960204    -4.17   0.000    -55.13487   -19.50429
       gg_rt |          0  (omitted)
     gg_http |   1.343441   2.180588     0.62   0.539    -2.992153    5.679035
       _cons |   51.27913   8.412618     6.10   0.000     34.55259    68.00566
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe favcount post_opo_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_
> rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   7,     85) =       4.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.6652
                                                  Adj R-squared   =     0.6546
                                                  Within R-sq.    =     0.0462
Number of clusters (userid)  =         86         Root MSE        =   300.8739

                                (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
    favcount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
post_opo_ada |  -96.61812   53.22118    -1.82   0.073    -202.4361    9.199845
     hour_sq |   .0748051   .1212204     0.62   0.539    -.1662136    .3158238
        hour |  -1.270213   3.216611    -0.39   0.694    -7.665697    5.125271
  gg_hashtag |  -91.41286   33.48683    -2.73   0.008    -157.9937   -24.83206
       gg_at |  -30.72018   17.71417    -1.73   0.087     -65.9407    4.500338
    gg_reply |  -144.1509   34.44139    -4.19   0.000    -212.6296   -75.67216
       gg_rt |          0  (omitted)
     gg_http |  -6.928823   14.90888    -0.46   0.643    -36.57167    22.71402
       _cons |   255.8504   39.83836     6.42   0.000      176.641    335.0597
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA21.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada) stats(N pFE dFE controls, fmt(0) l
> abels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violent attack and Twitter engagement (levels)") repla
> ce nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA21.tex)

. 
. 
. 
. 
. 
. 
. ***********FIGURES************
. 
. 
. 
. 
. **** FIGURE 2 ****
. 
. reghdfe log_engagement ib1014.event_days_ada_M##opposition if timeWindow3_ada & !gg_rt, cl(userid) abs(userid)
(dropped 2 singleton observations)
note: 1bn.opposition is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: 1.opposition omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 1 HDFE group                            F(  58,     85) =      54.08
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5662
                                                  Adj R-squared   =     0.5501
                                                  Within R-sq.    =     0.0773
Number of clusters (userid)  =         86         Root MSE        =     1.4766

                                               (Std. Err. adjusted for 86 clusters in userid)
---------------------------------------------------------------------------------------------
                            |               Robust
             log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
           event_days_ada_M |
                       985  |  -2.295298   .4782522    -4.80   0.000    -3.246192   -1.344405
                       986  |  -1.111894   .5502378    -2.02   0.046    -2.205914   -.0178736
                       987  |  -.1703617   .4276986    -0.40   0.691    -1.020741    .6800177
                       988  |   .0433964    .463526     0.09   0.926    -.8782174    .9650102
                       989  |  -.4094397    .447315    -0.92   0.363    -1.298822    .4799423
                       990  |  -.5252578   .4148933    -1.27   0.209    -1.350177    .2996613
                       991  |  -.2454051   .4071124    -0.60   0.548    -1.054854    .5640435
                       992  |   .0142449   .2847454     0.05   0.960    -.5519052     .580395
                       993  |  -.0435627   .3864902    -0.11   0.911    -.8120087    .7248834
                       994  |   -.488714   .3416562    -1.43   0.156    -1.168018      .19059
                       995  |  -.3889646   .3497719    -1.11   0.269    -1.084405    .3064757
                       996  |   .2472417   .3050031     0.81   0.420    -.3591862    .8536696
                       997  |  -.4379122   .4034974    -1.09   0.281    -1.240173    .3643487
                       998  |  -.3437918   .3562313    -0.97   0.337    -1.052075    .3644915
                       999  |  -.0432455   .4053554    -0.11   0.915    -.8492006    .7627096
                      1000  |   .5391856   .3558326     1.52   0.133     -.168305    1.246676
                      1001  |   .3450632   .3778739     0.91   0.364    -.4062513    1.096378
                      1002  |   .1116993   .3671637     0.30   0.762    -.6183206    .8417191
                      1003  |     .30415   .3654928     0.83   0.408    -.4225476    1.030848
                      1004  |   .1349442   .3659028     0.37   0.713    -.5925685    .8624569
                      1005  |  -.0993797   .3481092    -0.29   0.776    -.7915141    .5927547
                      1006  |  -.0510371    .372035    -0.14   0.891    -.7907423    .6886681
                      1007  |   -.085902   .3757262    -0.23   0.820    -.8329464    .6611423
                      1008  |  -.0841602   .2752705    -0.31   0.761    -.6314717    .4631514
                      1009  |     .09085   .3509432     0.26   0.796    -.6069191     .788619
                      1010  |   .0737049   .4140765     0.18   0.859    -.7495902    .8969999
                      1011  |   -.161194   .3780493    -0.43   0.671    -.9128572    .5904692
                      1012  |  -.0265619   .5159973    -0.05   0.959    -1.052503    .9993789
                      1013  |    .338524   .4780558     0.71   0.481    -.6119791    1.289027
                            |
               1.opposition |          0  (omitted)
                            |
event_days_ada_M#opposition |
                     985 1  |   1.833351   .6003115     3.05   0.003     .6397706    3.026931
                     986 1  |  -.0166281   1.037012    -0.02   0.987    -2.078485    2.045229
                     987 1  |   .5665248   .5909257     0.96   0.340    -.6083937    1.741443
                     988 1  |   .4755909   .6369247     0.75   0.457     -.790786    1.741968
                     989 1  |   1.167615   .5376475     2.17   0.033     .0986277    2.236602
                     990 1  |   .9505075   .6134144     1.55   0.125    -.2691247     2.17014
                     991 1  |    .811008   .4804767     1.69   0.095    -.1443085    1.766324
                     992 1  |   .3303829    .559505     0.59   0.556    -.7820628    1.442829
                     993 1  |  -.3541751   .5239607    -0.68   0.501    -1.395949    .6875991
                     994 1  |   .5099037   .6598183     0.77   0.442    -.8019919    1.821799
                     995 1  |   1.128618   .4987482     2.26   0.026     .1369732    2.120263
                     996 1  |  -.0024724   .5698275    -0.00   0.997    -1.135442    1.130497
                     997 1  |   .7971722   .4974838     1.60   0.113    -.1919588    1.786303
                     998 1  |   .6024958   .4854364     1.24   0.218    -.3626819    1.567673
                     999 1  |   1.000715     .49949     2.00   0.048     .0075946    1.993834
                    1000 1  |   .1270963   .4216837     0.30   0.764    -.7113239    .9655165
                    1001 1  |   .6284543   .4700235     1.34   0.185    -.3060783    1.562987
                    1002 1  |   .7083413   .5141256     1.38   0.172    -.3138781    1.730561
                    1003 1  |  -.0609443   .5615716    -0.11   0.914    -1.177499    1.055611
                    1004 1  |   .3402511   .6412086     0.53   0.597    -.9346434    1.615146
                    1005 1  |   .8201502   .5470247     1.50   0.138    -.2674815    1.907782
                    1006 1  |   1.249267   .5234193     2.39   0.019      .208569    2.289965
                    1007 1  |   .3083855   .5894699     0.52   0.602    -.8636386     1.48041
                    1008 1  |   .0825221   .4862037     0.17   0.866     -.884181    1.049225
                    1009 1  |   .7894612   .4647185     1.70   0.093    -.1345236    1.713446
                    1010 1  |   .1759328   .5632523     0.31   0.756    -.9439636    1.295829
                    1011 1  |   .4909926    .568032     0.86   0.390    -.6384072    1.620392
                    1012 1  |   .2499784   .6230224     0.40   0.689    -.9887571    1.488714
                    1013 1  |   .0023076   .5903828     0.00   0.997    -1.171532    1.176147
                            |
                      _cons |   2.661966   .2022934    13.16   0.000     2.259753     3.06418
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>30
(33 observations deleted)

.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen opo = (days>30)

.         replace days = days-30 if opo
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !opo
  3.                 replace coef = coef+r(mean) if opo & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -2.295298           .  -2.295298  -2.295298
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -1.111894           .  -1.111894  -1.111894
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1703617           .  -.1703617  -.1703617
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0433964           .   .0433964   .0433964
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.4094397           .  -.4094397  -.4094397
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5252578           .  -.5252578  -.5252578
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2454051           .  -.2454051  -.2454051
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0142449           .   .0142449   .0142449
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0435627           .  -.0435627  -.0435627
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.488714           .   -.488714   -.488714
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3889646           .  -.3889646  -.3889646
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2472417           .   .2472417   .2472417
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.4379122           .  -.4379122  -.4379122
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3437918           .  -.3437918  -.3437918
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0432455           .  -.0432455  -.0432455
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5391856           .   .5391856   .5391856
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3450632           .   .3450632   .3450632
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1116993           .   .1116993   .1116993
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1      .30415           .     .30415     .30415
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1349442           .   .1349442   .1349442
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0993797           .  -.0993797  -.0993797
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0510371           .  -.0510371  -.0510371
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.085902           .   -.085902   -.085902
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0841601           .  -.0841601  -.0841601
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1      .09085           .     .09085     .09085
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0737049           .   .0737049   .0737049
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.161194           .   -.161194   -.161194
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0265619           .  -.0265619  -.0265619
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .338524           .    .338524    .338524
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==-1 & !opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3437918           .  -.3437918  -.3437918

.         gen coef_n = coef-r(mean) if !opo
(31 missing values generated)

. 
.         sum coef if days==-1 & opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .258704           .    .258704    .258704

.         replace coef_n = coef-r(mean) if opo
(30 real changes made)

. 
. 
.         graph twoway (lpoly coef_n days if opo & days<0, lp(dash) bwidth(7) color(orange) msize(small) graphregion(color(white)) leg
> end( cols(1) order(1 "Opposition" 3 "Government")) ytitle("Tweet engagement", size(large)) xtitle("Days since event", size(large)) l
> width(0.3 0.3) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpoly coef_n days if opo & days>0, lp(dash) bwidth(7) color
> (orange) msize(small)) (lpoly coef_n days if !opo & days<0, bwidth(7) lp(line) color(navy) msize(small)) (lpoly coef_n days if !opo 
> & days>0, bwidth(7) lp(line) color(navy) msize(small)) (lpolyci coef_n days if opo & days<0, bwidth(7) pwidth(7) level(90) lwidth(no
> ne) color(orange%20)) (lpolyci coef_n days if opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(orange%20)) (lpolyci co
> ef_n days if !opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20)) (lpolyci coef_n days if !opo & days<0, bwidth
> (7) pwidth(7) level(90) lwidth(none) color(navy%20)) (scatter coef_n days if !opo & coef_n>-1, msymbol(square) color(navy) msize(sma
> ll)) (scatter coef_n days if opo & coef_n>-1, color(orange) msymbol(triangle) msize(small))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.         graph export "${PathFig}Figure2.png", replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure2.png written in PNG format)

. 
. restore

. 
. 
. **** FIGURE 4 ****
. 
. 
. reghdfe log_engagement ib1014.event_days_ada_M##mentionOther if timeWindow3_ada & !gg_rt, abs(userid)
(dropped 2 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 1 HDFE group                            F(  59,   3832) =      11.07
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5984
                                                  Adj R-squared   =     0.5833
                                                  Within R-sq.    =     0.1456
                                                  Root MSE        =     1.4211

-----------------------------------------------------------------------------------------------
               log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
             event_days_ada_M |
                         985  |   -1.59494   .2168179    -7.36   0.000     -2.02003   -1.169851
                         986  |  -1.466962   .2071064    -7.08   0.000    -1.873012   -1.060913
                         987  |   -.038805   .2093732    -0.19   0.853    -.4492985    .3716886
                         988  |   .2366363   .2769695     0.85   0.393    -.3063856    .7796581
                         989  |  -.0262723   .2400489    -0.11   0.913    -.4969082    .4443636
                         990  |  -.2802212   .2371296    -1.18   0.237    -.7451335    .1846912
                         991  |  -.0174289   .2146623    -0.08   0.935    -.4382924    .4034345
                         992  |    .058335    .206815     0.28   0.778    -.3471431    .4638131
                         993  |  -.2102354    .227005    -0.93   0.354    -.6552976    .2348269
                         994  |  -.2931004   .2440974    -1.20   0.230    -.7716737     .185473
                         995  |   .1456379   .2222591     0.66   0.512    -.2901195    .5813953
                         996  |  -.0901748   .2099945    -0.43   0.668    -.5018865     .321537
                         997  |  -.2844822   .2196269    -1.30   0.195    -.7150791    .1461146
                         998  |  -.1794667   .2079945    -0.86   0.388    -.5872573    .2283239
                         999  |   .5119765    .230716     2.22   0.027     .0596387    .9643144
                        1000  |   .6523621   .1808879     3.61   0.000     .2977163    1.007008
                        1001  |   .7924788   .1901743     4.17   0.000     .4196263    1.165331
                        1002  |   .5917196   .1926506     3.07   0.002     .2140121    .9694271
                        1003  |   .5781128   .1979943     2.92   0.004     .1899285     .966297
                        1004  |   .4569593   .1977226     2.31   0.021     .0693077     .844611
                        1005  |   .4308078   .1986335     2.17   0.030     .0413703    .8202453
                        1006  |   .5989062   .2011598     2.98   0.003     .2045156    .9932968
                        1007  |   .3127842   .1895566     1.65   0.099    -.0588572    .6844257
                        1008  |   .0094441   .1977874     0.05   0.962    -.3783346    .3972228
                        1009  |   .5022858   .1888152     2.66   0.008     .1320979    .8724736
                        1010  |   .2786576   .2014608     1.38   0.167    -.1163231    .6736383
                        1011  |   .0284633   .1915655     0.15   0.882    -.3471168    .4040434
                        1012  |   .1290316     .19681     0.66   0.512    -.2568308     .514894
                        1013  |   .3688718   .1855547     1.99   0.047     .0050763    .7326674
                              |
               1.mentionOther |    .749444   .3345226     2.24   0.025     .0935845    1.405303
                              |
event_days_ada_M#mentionOther |
                       985 1  |   2.509349   .5425437     4.63   0.000     1.445647    3.573051
                       986 1  |   3.618815   .6261392     5.78   0.000     2.391217    4.846413
                       987 1  |   1.016112   .5042528     2.02   0.044     .0274825    2.004742
                       988 1  |   .5119485    .652863     0.78   0.433    -.7680437    1.791941
                       989 1  |   .6640069   .4595136     1.45   0.149    -.2369077    1.564922
                       990 1  |   .6414594   .4469412     1.44   0.151    -.2348061    1.517725
                       991 1  |   .6273489      .4546     1.38   0.168    -.2639323     1.51863
                       992 1  |   .7915441   .4752807     1.67   0.096    -.1402831    1.723371
                       993 1  |  -.0692966   .5588801    -0.12   0.901    -1.165028    1.026434
                       994 1  |    .285323   .4886686     0.58   0.559    -.6727524    1.243398
                       995 1  |    .330265   .4432745     0.75   0.456    -.5388116    1.199341
                       996 1  |   .7522847    .430276     1.75   0.080    -.0913072    1.595877
                       997 1  |   1.377864   .4711144     2.92   0.003     .4542048    2.301523
                       998 1  |   .5994723   .4421853     1.36   0.175    -.2674687    1.466413
                       999 1  |   .1998849   .4972444     0.40   0.688    -.7750042    1.174774
                      1000 1  |  -.1056654   .4655074    -0.23   0.820    -1.018331    .8070005
                      1001 1  |  -.0667919   .6163243    -0.11   0.914    -1.275147    1.141563
                      1002 1  |  -.5811329   .4371197    -1.33   0.184    -1.438143    .2758767
                      1003 1  |  -.6318885    .440071    -1.44   0.151    -1.494684    .2309073
                      1004 1  |  -.1552237    .438673    -0.35   0.723    -1.015279    .7048312
                      1005 1  |  -.7636595   .5344091    -1.43   0.153    -1.811413    .2840941
                      1006 1  |  -.8534002   .5341962    -1.60   0.110    -1.900736    .1939359
                      1007 1  |  -.9264404   .4214881    -2.20   0.028    -1.752803   -.1000779
                      1008 1  |   .1085431   .5487468     0.20   0.843    -.9673206    1.184407
                      1009 1  |   .1371652   .4433907     0.31   0.757    -.7321391     1.00647
                      1010 1  |  -.1792703   .4703166    -0.38   0.703    -1.101365    .7428245
                      1011 1  |   .4723056   .4306493     1.10   0.273    -.3720182    1.316629
                      1012 1  |   .0847127   .4593896     0.18   0.854    -.8159589    .9853843
                      1013 1  |   .9060777   .5323626     1.70   0.089    -.1376635    1.949819
                              |
                        _cons |   2.493914   .1444706    17.26   0.000     2.210667     2.77716
-----------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86           0          86     |
-----------------------------------------------------+

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>29
(33 observations deleted)

.         /*we drop in 29 instead of 30 because of extra 0*/
.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 30
(1 real change made)

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen menOt = (days>30)

.         replace days = days-30 if menOt
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !menOt
  3.                 replace coef = coef+r(mean) if menOt & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -1.59494           .   -1.59494   -1.59494
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -1.466962           .  -1.466962  -1.466962
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.038805           .   -.038805   -.038805
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2366363           .   .2366363   .2366363
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0262723           .  -.0262723  -.0262723
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2802212           .  -.2802212  -.2802212
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0174289           .  -.0174289  -.0174289
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .058335           .    .058335    .058335
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2102354           .  -.2102354  -.2102354
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2931004           .  -.2931004  -.2931004
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1456379           .   .1456379   .1456379
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0901748           .  -.0901748  -.0901748
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2844822           .  -.2844822  -.2844822
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1794667           .  -.1794667  -.1794667
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5119765           .   .5119765   .5119765
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .6523621           .   .6523621   .6523621
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .7924788           .   .7924788   .7924788
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5917196           .   .5917196   .5917196
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5781128           .   .5781128   .5781128
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4569593           .   .4569593   .4569593
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4308078           .   .4308078   .4308078
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5989062           .   .5989062   .5989062
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3127842           .   .3127842   .3127842
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0094441           .   .0094441   .0094441
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5022858           .   .5022858   .5022858
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2786576           .   .2786576   .2786576
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0284633           .   .0284633   .0284633
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1290316           .   .1290316   .1290316
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3688718           .   .3688718   .3688718
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==-1 & !menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1794667           .  -.1794667  -.1794667

.         gen coef_n = coef-r(mean) if !menOt
(31 missing values generated)

. 
.         sum coef if days==-1 & menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4200056           .   .4200056   .4200056

.         replace coef_n = coef-r(mean) if menOt
(30 real changes made)

. 
. 
.         graph twoway (lpoly coef_n days if menOt & days<0, lp(dash) bwidth(7) color(red) msize(small) graphregion(color(white)) lege
> nd( cols(1) order(1 "Mention rival" 3 "Not mention rival")) ytitle("Tweet engagement", size(large)) xtitle("Days since event", size(
> large)) lwidth(0.3 0.3) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpoly coef_n days if menOt & days>0, lp(dash) bwid
> th(7) color(red) msize(small)) (lpoly coef_n days if !menOt & days<0, bwidth(7) lp(line) color(eltgreen) msize(small)) (lpoly coef_n
>  days if !menOt & days>0, bwidth(7) lp(line) color(eltgreen) msize(small)) (lpolyci coef_n days if menOt & days<0, bwidth(7) pwidth(
> 7) level(90) lwidth(none) color(red%20)) (lpolyci coef_n days if menOt & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(re
> d%20)) (lpolyci coef_n days if !menOt & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20)) (lpolyci coef_n days 
> if !menOt & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20)) (scatter coef_n days if !menOt & coef_n>-1 & coef
> _n<1, msymbol(Oh) color(eltgreen) msize(small)) (scatter coef_n days if menOt & coef_n>-1 & coef_n<1, color(red) msymbol(X) msize(me
> dium))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.                 graph export "${PathFig}Figure4.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure4.png written in PNG format)

. restore

. 
. 
. 
. ***Figure A3 **** 
. 
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada##post_mp_ada  hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http 
> if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      15.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7379
                                                  Adj R-squared   =     0.7295
                                                  Within R-sq.    =     0.4071
Number of clusters (userid)  =         86         Root MSE        =     1.1450

                                            (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------------------
                         |               Robust
          log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
          1.post_opo_ada |  -1.162237   .4839531    -2.40   0.019    -2.124466   -.2000088
           1.post_mp_ada |  -.4675163   .3936543    -1.19   0.238    -1.250206    .3151739
                         |
post_opo_ada#post_mp_ada |
                    1 1  |   .9529816    .510801     1.87   0.066    -.0626276    1.968591
                         |
                 hour_sq |  -.0013304   .0006496    -2.05   0.044    -.0026221   -.0000388
                    hour |   .0303489   .0172221     1.76   0.082    -.0038932    .0645911
              gg_hashtag |  -.1599181   .1462083    -1.09   0.277    -.4506193    .1307832
                   gg_at |  -.4628524   .1138731    -4.06   0.000    -.6892627   -.2364421
                gg_reply |  -1.939611   .3161816    -6.13   0.000    -2.568264   -1.310957
                   gg_rt |          0  (omitted)
                 gg_http |   .3206852   .0777441     4.12   0.000     .1661091    .4752613
                   _cons |   3.731791   .2305957    16.18   0.000     3.273305    4.190277
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. margins, dydx(post_opo_ada) at(post_mp_ada=(0(1)1))

Average marginal effects                        Number of obs     =      3,977
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 1.post_opo_ada

1._at        : post_mp_ada     =           0

2._at        : post_mp_ada     =           1

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
0.post_opo_ada  |  (base outcome)
----------------+----------------------------------------------------------------
1.post_opo_ada  |
            _at |
             1  |  -1.162237   .4839531    -2.40   0.016    -2.110768   -.2137065
             2  |  -.2092556   .1446937    -1.45   0.148      -.49285    .0743388
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, recast(dot) yline(0) level(90) saving(margins1.gph, replace)

  Variables that uniquely identify margins: post_mp_ada
(file margins1.gph saved)

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada##post_mp_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWind
> ow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      13.87
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7260
                                                  Adj R-squared   =     0.7172
                                                  Within R-sq.    =     0.4376
Number of clusters (userid)  =         86         Root MSE        =     0.9369

                                            (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------------------
                         |               Robust
                  log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
          1.post_opo_ada |  -.9114068   .3360177    -2.71   0.008      -1.5795   -.2433135
           1.post_mp_ada |  -.1760135   .1934443    -0.91   0.365    -.5606327    .2086057
                         |
post_opo_ada#post_mp_ada |
                    1 1  |   .6815449   .3584112     1.90   0.061    -.0310726    1.394162
                         |
                 hour_sq |  -.0013603   .0005491    -2.48   0.015    -.0024521   -.0002684
                    hour |   .0266728   .0147805     1.80   0.075    -.0027148    .0560605
              gg_hashtag |  -.0829597    .139023    -0.60   0.552    -.3593747    .1934552
                   gg_at |  -.3476389   .0997356    -3.49   0.001    -.5459399   -.1493379
                gg_reply |  -1.715645   .2692328    -6.37   0.000    -2.250952   -1.180338
                   gg_rt |          0  (omitted)
                 gg_http |   .2964327    .053024     5.59   0.000     .1910067    .4018587
                   _cons |   2.340216   .1514287    15.45   0.000     2.039135    2.641297
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. margins, dydx(post_opo_ada) at(post_mp_ada=(0(1)1))

Average marginal effects                        Number of obs     =      3,977
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 1.post_opo_ada

1._at        : post_mp_ada     =           0

2._at        : post_mp_ada     =           1

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
0.post_opo_ada  |  (base outcome)
----------------+----------------------------------------------------------------
1.post_opo_ada  |
            _at |
             1  |  -.9114068   .3360177    -2.71   0.007     -1.56999   -.2528242
             2  |   -.229862   .1138212    -2.02   0.043    -.4529475   -.0067764
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, recast(dot) yline(0) level(90) saving(margins2.gph, replace)

  Variables that uniquely identify margins: post_mp_ada
(file margins2.gph saved)

. eststo, title("Likes"): reghdfe log_fav post_opo_ada##post_mp_ada hour_sq hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow
> 3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(   9,     85) =      13.37
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7326
                                                  Adj R-squared   =     0.7240
                                                  Within R-sq.    =     0.3847
Number of clusters (userid)  =         86         Root MSE        =     1.1226

                                            (Std. Err. adjusted for 86 clusters in userid)
------------------------------------------------------------------------------------------
                         |               Robust
                 log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
          1.post_opo_ada |  -1.155019   .4997079    -2.31   0.023    -2.148572   -.1614657
           1.post_mp_ada |  -.4741875   .4074018    -1.16   0.248    -1.284211    .3358363
                         |
post_opo_ada#post_mp_ada |
                    1 1  |   .9580718   .5262889     1.82   0.072    -.0883316    2.004475
                         |
                 hour_sq |  -.0011959   .0006226    -1.92   0.058    -.0024339     .000042
                    hour |   .0284065   .0165927     1.71   0.091    -.0045842    .0613973
              gg_hashtag |  -.1844443   .1419194    -1.30   0.197     -.466618    .0977294
                   gg_at |  -.4367681   .1076986    -4.06   0.000    -.6509017   -.2226345
                gg_reply |  -1.834198   .3060687    -5.99   0.000    -2.442745   -1.225652
                   gg_rt |          0  (omitted)
                 gg_http |    .269473   .0780622     3.45   0.001     .1142645    .4246815
                   _cons |   3.565973   .2330048    15.30   0.000     3.102697    4.029248
------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. margins, dydx(post_opo_ada) at(post_mp_ada=(0(1)1))

Average marginal effects                        Number of obs     =      3,977
Model VCE    : Robust

Expression   : Linear prediction, predict()
dy/dx w.r.t. : 1.post_opo_ada

1._at        : post_mp_ada     =           0

2._at        : post_mp_ada     =           1

---------------------------------------------------------------------------------
                |            Delta-method
                |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
0.post_opo_ada  |  (base outcome)
----------------+----------------------------------------------------------------
1.post_opo_ada  |
            _at |
             1  |  -1.155019   .4997079    -2.31   0.021    -2.134428   -.1756094
             2  |  -.1969469   .1440596    -1.37   0.172    -.4792985    .0854047
---------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. marginsplot, recast(dot) yline(0) level(90) saving(margins3.gph, replace)

  Variables that uniquely identify margins: post_mp_ada
(file margins3.gph saved)

. graph combine margins1.gph margins2.gph  margins3.gph, xcommon ycommon

. 
. 
. 
. **** FIGURE A4 ****
. 
. 
. reghdfe log_engagement ib1014.event_days_placebo_M##opposition if timeWindow3_placebo & !gg_rt, cl(userid) abs(userid)
(dropped 10 singleton observations)
note: 1bn.opposition is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: 1.opposition omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,179
Absorbing 1 HDFE group                            F(  58,     73) =      64.89
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5272
                                                  Adj R-squared   =     0.5069
                                                  Within R-sq.    =     0.0861
Number of clusters (userid)  =         74         Root MSE        =     1.5199

                                                   (Std. Err. adjusted for 74 clusters in userid)
-------------------------------------------------------------------------------------------------
                                |               Robust
                 log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
           event_days_placebo_M |
                           985  |  -.0137442   .3402755    -0.04   0.968    -.6919122    .6644238
                           986  |   .1986135   .3716192     0.53   0.595    -.5420225    .9392495
                           987  |   .4688476   .3360404     1.40   0.167    -.2008799    1.138575
                           988  |  -.1944521    .332082    -0.59   0.560    -.8562905    .4673863
                           989  |  -.3629972   .3892646    -0.93   0.354      -1.1388     .412806
                           990  |   -.420051   .3108741    -1.35   0.181    -1.039622    .1995203
                           991  |  -.0423805   .3665715    -0.12   0.908    -.7729564    .6881954
                           992  |   .1508205   .3286579     0.46   0.648    -.5041938    .8058348
                           993  |   -.615783   .4305161    -1.43   0.157      -1.4738    .2422345
                           994  |  -.5108527   .5743644    -0.89   0.377    -1.655559    .6338539
                           995  |   .5307541   .2965419     1.79   0.078     -.060253    1.121761
                           996  |   .7055249   .5984594     1.18   0.242    -.4872031    1.898253
                           997  |  -.2165676   .4175907    -0.52   0.606    -1.048825    .6156894
                           998  |   .1888454   .3645586     0.52   0.606    -.5377188    .9154097
                           999  |  -.5053655   .4306607    -1.17   0.244    -1.363671      .35294
                          1000  |  -1.280635   .4861557    -2.63   0.010    -2.249542   -.3117278
                          1001  |  -.6624221   .6856964    -0.97   0.337    -2.029013    .7041689
                          1002  |  -.1532848   .4330365    -0.35   0.724    -1.016325    .7097557
                          1003  |  -.1014078   .4235995    -0.24   0.811    -.9456403    .7428248
                          1004  |  -.3766137   .3615544    -1.04   0.301    -1.097191    .3439632
                          1005  |  -.3696944   .5137028    -0.72   0.474    -1.393503    .6541138
                          1006  |  -.0858632   .3519328    -0.24   0.808    -.7872642    .6155378
                          1007  |   .1801745   .3736078     0.48   0.631    -.5644249    .9247738
                          1008  |   .1355006   .3272533     0.41   0.680    -.5167143    .7877155
                          1009  |  -.2792427   .3813422    -0.73   0.466    -1.039257    .4807713
                          1010  |  -.0512339   .3263079    -0.16   0.876    -.7015646    .5990968
                          1011  |   .4010506   .2992351     1.34   0.184    -.1953242    .9974253
                          1012  |  -.2938382   .3815775    -0.77   0.444    -1.054321    .4666447
                          1013  |  -.2724074   .3112241    -0.88   0.384    -.8926762    .3478614
                                |
                   1.opposition |          0  (omitted)
                                |
event_days_placebo_M#opposition |
                         985 1  |  -.3849953   .4115125    -0.94   0.353    -1.205139    .4351479
                         986 1  |  -.3594059   .4516297    -0.80   0.429    -1.259502    .5406908
                         987 1  |  -1.176903    .531132    -2.22   0.030    -2.235447   -.1183584
                         988 1  |  -.4765565   .3828683    -1.24   0.217    -1.239612     .286499
                         989 1  |  -.3793358   .4826858    -0.79   0.434    -1.341327    .5826556
                         990 1  |  -.0772649    .487977    -0.16   0.875    -1.049802    .8952718
                         991 1  |   .1810009   .4173639     0.43   0.666    -.6508041    1.012806
                         992 1  |  -.7400206   .4344437    -1.70   0.093    -1.605866    .1258244
                         993 1  |  -.0014189    .469994    -0.00   0.998    -.9381156    .9352779
                         994 1  |  -.5080025   .6394607    -0.79   0.430    -1.782446    .7664408
                         995 1  |   -1.75065   .4641068    -3.77   0.000    -2.675614    -.825687
                         996 1  |  -1.411216   .7483698    -1.89   0.063    -2.902715    .0802828
                         997 1  |   -.908997   .5309224    -1.71   0.091    -1.967124    .1491299
                         998 1  |  -.8665529   .4369549    -1.98   0.051    -1.737403    .0042971
                         999 1  |  -.2059682    .504899    -0.41   0.685     -1.21223     .800294
                        1000 1  |  -.2389178   .5846678    -0.41   0.684    -1.404159    .9263234
                        1001 1  |  -1.949624   1.207842    -1.61   0.111    -4.356849    .4576008
                        1002 1  |  -.5241139   .5139626    -1.02   0.311     -1.54844     .500212
                        1003 1  |  -.3189083   .6508701    -0.49   0.626    -1.616091     .978274
                        1004 1  |    .176183   .4185692     0.42   0.675    -.6580242     1.01039
                        1005 1  |  -.1647556   .6262654    -0.26   0.793    -1.412901    1.083389
                        1006 1  |  -.3523157   .4325187    -0.81   0.418    -1.214324    .5096927
                        1007 1  |  -.8695591     .57515    -1.51   0.135    -2.015831    .2767132
                        1008 1  |  -1.334302   .4817429    -2.77   0.007    -2.294415   -.3741902
                        1009 1  |  -.6099833   .6045178    -1.01   0.316    -1.814786     .594819
                        1010 1  |  -.2191461   .4183244    -0.52   0.602    -1.052865    .6145732
                        1011 1  |   -1.02416   .5247354    -1.95   0.055    -2.069956    .0216364
                        1012 1  |  -.3571996   .4727616    -0.76   0.452    -1.299412     .585013
                        1013 1  |  -.4477045   .4308355    -1.04   0.302    -1.306359    .4109495
                                |
                          _cons |   3.618465   .1669254    21.68   0.000     3.285783    3.951147
-------------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        74          74           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>30
(33 observations deleted)

.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen opo = (days>30)

.         replace days = days-30 if opo
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !opo
  3.                 replace coef = coef+r(mean) if opo & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0137442           .  -.0137442  -.0137442
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1986135           .   .1986135   .1986135
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4688476           .   .4688476   .4688476
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1944521           .  -.1944521  -.1944521
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3629972           .  -.3629972  -.3629972
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.4200509           .  -.4200509  -.4200509
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0423805           .  -.0423805  -.0423805
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1508205           .   .1508205   .1508205
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.615783           .   -.615783   -.615783
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5108526           .  -.5108526  -.5108526
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5307541           .   .5307541   .5307541
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .7055249           .   .7055249   .7055249
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2165676           .  -.2165676  -.2165676
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1888454           .   .1888454   .1888454
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5053655           .  -.5053655  -.5053655
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -1.280635           .  -1.280635  -1.280635
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.6624221           .  -.6624221  -.6624221
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1532848           .  -.1532848  -.1532848
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1014078           .  -.1014078  -.1014078
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3766137           .  -.3766137  -.3766137
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3696944           .  -.3696944  -.3696944
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0858632           .  -.0858632  -.0858632
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1801745           .   .1801745   .1801745
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1355006           .   .1355006   .1355006
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2792427           .  -.2792427  -.2792427
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0512339           .  -.0512339  -.0512339
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4010506           .   .4010506   .4010506
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2938382           .  -.2938382  -.2938382
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2724074           .  -.2724074  -.2724074
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==14 & !opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2724074           .  -.2724074  -.2724074

.         gen coef_n = coef-r(mean) if !opo
(31 missing values generated)

. 
.         sum coef if days==14 & opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.7201118           .  -.7201118  -.7201118

.         replace coef_n = coef-r(mean) if opo
(30 real changes made)

. 
. 
.         graph twoway (lpoly coef_n days if opo & days<0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if opo & 
> days>0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if !opo & days<0, bwidth(7) lp(line) color(navy) msize(sma
> ll)) (lpoly coef_n days if !opo & days>0, bwidth(7) lp(line) color(navy) msize(small)) (scatter coef_n days if !opo & coef_n>-1, msy
> mbol(square) color(navy) msize(small)) (scatter coef_n days if opo & coef_n>-1, msymbol(triangle) msize(small) graphregion(color(whi
> te)) legend( cols(1) order(6 "Opposition" 5 "Government")) ytitle("Tweet engagement", size(large)) xtitle("Days since event", size(l
> arge)) lwidth(0.3 0.3) color(orange) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpolyci coef_n days if opo & days<0, 
> bwidth(7) pwidth(7) level(90) lwidth(none) color(orange%20)) (lpolyci coef_n days if opo & days>0, bwidth(7) pwidth(7) level(90) lwi
> dth(none) color(orange%20)) (lpolyci coef_n days if !opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20)) (lpoly
> ci coef_n days if !opo & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.                 graph export "${PathFig}FigureA4.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA4.png written in PNG format)

. restore

. 
. 
. 
. **** FIGURE A5 ****
. 
. 
. reghdfe log_engagement ib1014.event_days_wosp_M##opposition if timeWindow3_wosp & !gg_rt, cl(userid) abs(userid)
(dropped 3 singleton observations)
note: 1bn.opposition is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters
note: 1.opposition omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,946
Absorbing 1 HDFE group                            F(  58,     65) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7201
                                                  Adj R-squared   =     0.7012
                                                  Within R-sq.    =     0.0536
Number of clusters (userid)  =         66         Root MSE        =     1.2120

                                                (Std. Err. adjusted for 66 clusters in userid)
----------------------------------------------------------------------------------------------
                             |               Robust
              log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
           event_days_wosp_M |
                        985  |   .2446668   .1659087     1.47   0.145    -.0866757    .5760093
                        986  |   .2939499   .2458409     1.20   0.236    -.1970283    .7849281
                        987  |   .1528601   .4027923     0.38   0.706    -.6515718    .9572919
                        988  |  -.2714853   .9608393    -0.28   0.778    -2.190414    1.647443
                        989  |  -.2416241   .3346936    -0.72   0.473    -.9100534    .4268051
                        990  |  -.0544452   .3592665    -0.15   0.880    -.7719499    .6630595
                        991  |   -.157237    .279226    -0.56   0.575    -.7148898    .4004158
                        992  |   .2133724   .3544618     0.60   0.549    -.4945367    .9212815
                        993  |   .0256368   .3076084     0.08   0.934    -.5886995    .6399731
                        994  |   -.156095     .34516    -0.45   0.653     -.845427    .5332371
                        995  |   .5795544   .4437664     1.31   0.196    -.3067083    1.465817
                        996  |   .6832652   .4224999     1.62   0.111    -.1605254    1.527056
                        997  |   .1743737   .3829231     0.46   0.650    -.5903766     .939124
                        998  |    .115925   .2630844     0.44   0.661    -.4094908    .6413407
                        999  |   .0155571   .2660923     0.06   0.954    -.5158659    .5469802
                       1000  |   .3870902   .3298483     1.17   0.245    -.2716625    1.045843
                       1001  |  -.5147746   .6339732    -0.81   0.420    -1.780907    .7513573
                       1002  |   .3670524   .3583454     1.02   0.309    -.3486128    1.082718
                       1003  |  -.0609414   .4153538    -0.15   0.884    -.8904603    .7685774
                       1004  |   .2289255   .2718578     0.84   0.403    -.3140121    .7718631
                       1005  |   .1223913   .2860612     0.43   0.670    -.4489124    .6936951
                       1006  |   .4057547   .4150928     0.98   0.332    -.4232429    1.234752
                       1007  |   .2698852   .3197646     0.84   0.402    -.3687289    .9084992
                       1008  |   .2082069   .3143328     0.66   0.510    -.4195591    .8359728
                       1009  |   .4753698    .274458     1.73   0.088    -.0727607      1.0235
                       1010  |   .3357684    .319133     1.05   0.297    -.3015843    .9731211
                       1011  |   .2308249   .3829644     0.60   0.549    -.5340078    .9956576
                       1012  |   .2671355    .337561     0.79   0.432    -.4070204    .9412914
                       1013  |   .2767145    .339629     0.81   0.418    -.4015716    .9550005
                             |
                1.opposition |          0  (omitted)
                             |
event_days_wosp_M#opposition |
                      985 1  |   .9898232   .4124128     2.40   0.019     .1661779    1.813469
                      986 1  |   .0736579   .4668183     0.16   0.875    -.8586425    1.005958
                      987 1  |  -.2558323   .6827931    -0.37   0.709    -1.619464      1.1078
                      988 1  |   .6492476   1.028082     0.63   0.530    -1.403974    2.702469
                      989 1  |   .5944634   .4507271     1.32   0.192    -.3057007    1.494628
                      990 1  |   .2372106   .5884027     0.40   0.688    -.9379107    1.412332
                      991 1  |   .5152739     .41535     1.24   0.219    -.3142373    1.344785
                      992 1  |  -.4001803   .5644479    -0.71   0.481    -1.527461       .7271
                      993 1  |   .2960984   .5238989     0.57   0.574    -.7501999    1.342397
                      994 1  |   .2476702   .5902113     0.42   0.676    -.9310632    1.426404
                      995 1  |   -.271604   .5626407    -0.48   0.631    -1.395275     .852067
                      996 1  |  -.2809258   .5395601    -0.52   0.604    -1.358502    .7966502
                      997 1  |   .0191645   .5138314     0.04   0.970    -1.007028    1.045357
                      998 1  |  -.1979099   .4979805    -0.40   0.692    -1.192446    .7966258
                      999 1  |   .5060896   .4421831     1.14   0.257    -.3770109     1.38919
                     1000 1  |   .5090572   .5419117     0.94   0.351    -.5732153     1.59133
                     1001 1  |   1.012889   .7257824     1.40   0.168    -.4365981    2.462377
                     1002 1  |   .0710889    .469291     0.15   0.880    -.8661499    1.008328
                     1003 1  |  -.2125046   .6285924    -0.34   0.736     -1.46789    1.042881
                     1004 1  |   .2278181   .4213659     0.54   0.591    -.6137076    1.069344
                     1005 1  |  -.1237748   .4382873    -0.28   0.779     -.999095    .7515455
                     1006 1  |  -.8733728   .5430038    -1.61   0.113    -1.957826    .2110807
                     1007 1  |  -.3952729   .6046064    -0.65   0.516    -1.602755    .8122093
                     1008 1  |   -.255627   .4297477    -0.59   0.554    -1.113892    .6026385
                     1009 1  |  -.4752462   .4479159    -1.06   0.293    -1.369796    .4193035
                     1010 1  |  -.0294602   .4289436    -0.07   0.945    -.8861197    .8271993
                     1011 1  |  -.2609488   .4841504    -0.54   0.592    -1.227864    .7059663
                     1012 1  |  -.4802498    .489243    -0.98   0.330    -1.457336    .4968361
                     1013 1  |  -.1043685   .5225076    -0.20   0.842    -1.147888    .9391512
                             |
                       _cons |   2.973109   .1984189    14.98   0.000      2.57684    3.369379
----------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        66          66           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>30
(33 observations deleted)

.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen opo = (days>30)

.         replace days = days-30 if opo
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !opo
  3.                 replace coef = coef+r(mean) if opo & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2446668           .   .2446668   .2446668
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2939499           .   .2939499   .2939499
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1528601           .   .1528601   .1528601
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2714853           .  -.2714853  -.2714853
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2416241           .  -.2416241  -.2416241
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0544452           .  -.0544452  -.0544452
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.157237           .   -.157237   -.157237
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2133724           .   .2133724   .2133724
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0256368           .   .0256368   .0256368
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.156095           .   -.156095   -.156095
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5795544           .   .5795544   .5795544
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .6832652           .   .6832652   .6832652
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1743737           .   .1743737   .1743737
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .115925           .    .115925    .115925
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0155571           .   .0155571   .0155571
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3870902           .   .3870902   .3870902
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5147746           .  -.5147746  -.5147746
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3670524           .   .3670524   .3670524
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0609414           .  -.0609414  -.0609414
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2289255           .   .2289255   .2289255
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1223913           .   .1223913   .1223913
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4057547           .   .4057547   .4057547
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2698852           .   .2698852   .2698852
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2082069           .   .2082069   .2082069
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4753698           .   .4753698   .4753698
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3357684           .   .3357684   .3357684
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2308249           .   .2308249   .2308249
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2671355           .   .2671355   .2671355
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2767145           .   .2767145   .2767145
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==14 & !opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2767145           .   .2767145   .2767145

.         gen coef_n = coef-r(mean) if !opo
(31 missing values generated)

. 
.         sum coef if days==14 & opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1723459           .   .1723459   .1723459

.         replace coef_n = coef-r(mean) if opo
(30 real changes made)

. 
. 
.         graph twoway (lpoly coef_n days if opo & days<0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if opo & 
> days>0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if !opo & days<0, bwidth(7) lp(line) color(navy) msize(sma
> ll)) (lpoly coef_n days if !opo & days>0, bwidth(7) lp(line) color(navy) msize(small)) (scatter coef_n days if !opo & coef_n>-1, msy
> mbol(square) color(navy) msize(small)) (scatter coef_n days if opo & coef_n>-1, msymbol(triangle) msize(small) graphregion(color(whi
> te)) legend( cols(1) order(6 "Opposition" 5 "Government")) ytitle("Tweet engagement", size(large)) xtitle("Days since event", size(l
> arge)) lwidth(0.3 0.3) color(orange) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpolyci coef_n days if opo & days<0, 
> bwidth(7) pwidth(7) level(90) lwidth(none) color(orange%20)) (lpolyci coef_n days if opo & days>0, bwidth(7) pwidth(7) level(90) lwi
> dth(none) color(orange%20)) (lpolyci coef_n days if !opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20)) (lpoly
> ci coef_n days if !opo & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.                 graph export "${PathFig}FigureA5.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA5.png written in PNG format)

. restore

. 
. 
. 
. **** FIGURE A6 ****
. 
. 
. reghdfe log_n_tweets ib1014.event_days_ada_M##opposition if _tweets==1 & timeWindow3_ada, cl(userid) abs(userid) 
(dropped 4 singleton observations)
note: 1bn.opposition is probably collinear with the fixed effects (all partialled-out values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: 1.opposition omitted because of collinearity

HDFE Linear regression                            Number of obs   =      1,587
Absorbing 1 HDFE group                            F(  58,     86) =       7.44
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5262
                                                  Adj R-squared   =     0.4789
                                                  Within R-sq.    =     0.1321
Number of clusters (userid)  =         87         Root MSE        =     0.7249

                                               (Std. Err. adjusted for 87 clusters in userid)
---------------------------------------------------------------------------------------------
                            |               Robust
               log_n_tweets |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
           event_days_ada_M |
                       985  |  -.5435985   .2847435    -1.91   0.060     -1.10965    .0224529
                       986  |  -.4926702   .2325768    -2.12   0.037    -.9550176   -.0303228
                       987  |   -.030324   .1874831    -0.16   0.872     -.403028      .34238
                       988  |  -.3863532   .2196757    -1.76   0.082     -.823054    .0503476
                       989  |  -.3013371   .2062609    -1.46   0.148    -.7113701     .108696
                       990  |  -.3208415   .2028478    -1.58   0.117    -.7240897    .0824066
                       991  |  -.0769122   .2130208    -0.36   0.719    -.5003836    .3465592
                       992  |   .0845486   .2119757     0.40   0.691    -.3368451    .5059423
                       993  |  -.1366938    .208408    -0.66   0.514    -.5509951    .2776075
                       994  |  -.3024018   .2068202    -1.46   0.147    -.7135468    .1087432
                       995  |   .0645051   .2201957     0.29   0.770    -.3732294    .5022396
                       996  |    .192476   .1940638     0.99   0.324    -.1933101    .5782621
                       997  |  -.2725898   .2200519    -1.24   0.219    -.7100385    .1648588
                       998  |   .0503152     .18018     0.28   0.781    -.3078707    .4085011
                       999  |   .1318636   .2797757     0.47   0.639     -.424312    .6880392
                      1000  |   .1753296   .2042466     0.86   0.393    -.2306992    .5813583
                      1001  |   -.050853   .2125596    -0.24   0.811    -.4734074    .3717015
                      1002  |  -.1481717   .2293471    -0.65   0.520    -.6040986    .3077552
                      1003  |  -.1785326   .2640578    -0.68   0.501    -.7034621     .346397
                      1004  |  -.2294644   .2245885    -1.02   0.310    -.6759317    .2170028
                      1005  |  -.2458871   .2411789    -1.02   0.311     -.725335    .2335607
                      1006  |   -.352052   .3030725    -1.16   0.249    -.9545403    .2504362
                      1007  |   .2801554   .2135862     1.31   0.193    -.1444399    .7047507
                      1008  |   .0447339   .1647791     0.27   0.787    -.2828361    .3723039
                      1009  |   .3555612   .2288633     1.55   0.124     -.099404    .8105265
                      1010  |     .28201   .1893804     1.49   0.140    -.0944657    .6584858
                      1011  |   .2860673   .2110813     1.36   0.179    -.1335485    .7056831
                      1012  |   .1881309   .2326585     0.81   0.421    -.2743789    .6506407
                      1013  |   .2366668   .2220356     1.07   0.289    -.2047253    .6780589
                            |
               1.opposition |          0  (omitted)
                            |
event_days_ada_M#opposition |
                     985 1  |  -.1556241   .3783425    -0.41   0.682    -.9077441    .5964959
                     986 1  |   .2189022   .3793922     0.58   0.565    -.5353045    .9731089
                     987 1  |  -.4706597   .2689449    -1.75   0.084    -1.005304    .0639851
                     988 1  |   -.174926   .3056239    -0.57   0.569    -.7824863    .4326342
                     989 1  |    .099735   .2801774     0.36   0.723    -.4572392    .6567092
                     990 1  |    .249036    .329314     0.76   0.452    -.4056184    .9036905
                     991 1  |  -.0568885   .2942925    -0.19   0.847    -.6419226    .5281457
                     992 1  |  -.4556509   .3132592    -1.45   0.149     -1.07839    .1670877
                     993 1  |  -.2674645   .2814171    -0.95   0.345    -.8269031    .2919742
                     994 1  |  -.0570332   .3159902    -0.18   0.857     -.685201    .5711345
                     995 1  |  -.2086189   .3070234    -0.68   0.499    -.8189612    .4017233
                     996 1  |   -.292857    .290257    -1.01   0.316    -.8698688    .2841549
                     997 1  |   .1265596   .2748068     0.46   0.646    -.4197383    .6728574
                     998 1  |  -.1750429   .2495536    -0.70   0.485    -.6711391    .3210533
                     999 1  |  -.5520236   .3494638    -1.58   0.118    -1.246735    .1426874
                    1000 1  |   .1199192   .2861276     0.42   0.676    -.4488836     .688722
                    1001 1  |   .0971344   .3183365     0.31   0.761    -.5356977    .7299665
                    1002 1  |  -.3325998   .3141685    -1.06   0.293    -.9571461    .2919465
                    1003 1  |   .3980934   .3207956     1.24   0.218     -.239627    1.035814
                    1004 1  |  -.2971477   .2964385    -1.00   0.319    -.8864479    .2921526
                    1005 1  |  -.2904955   .3212333    -0.90   0.368    -.9290862    .3480952
                    1006 1  |  -.2410539   .3933995    -0.61   0.542    -1.023106    .5409984
                    1007 1  |  -.7939098   .3077762    -2.58   0.012    -1.405749   -.1820709
                    1008 1  |  -.3580012   .2739455    -1.31   0.195    -.9025867    .1865844
                    1009 1  |  -.3562034   .3102432    -1.15   0.254    -.9729464    .2605396
                    1010 1  |  -.4869956   .2855251    -1.71   0.092    -1.054601    .0806096
                    1011 1  |  -.1763628   .2747025    -0.64   0.523    -.7224533    .3697277
                    1012 1  |  -.3201553   .3062353    -1.05   0.299    -.9289308    .2886203
                    1013 1  |   .4445666   .3280768     1.36   0.179    -.2076286    1.096762
                            |
                      _cons |    1.32068   .1130607    11.68   0.000     1.095923    1.545437
---------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        87          87           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>30
(33 observations deleted)

.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen opo = (days>30)

.         replace days = days-30 if opo
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !opo
  3.                 replace coef = coef+r(mean) if opo & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5435985           .  -.5435985  -.5435985
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.4926702           .  -.4926702  -.4926702
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.030324           .   -.030324   -.030324
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3863532           .  -.3863532  -.3863532
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3013371           .  -.3013371  -.3013371
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3208416           .  -.3208416  -.3208416
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0769122           .  -.0769122  -.0769122
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0845486           .   .0845486   .0845486
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1366938           .  -.1366938  -.1366938
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.3024018           .  -.3024018  -.3024018
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0645051           .   .0645051   .0645051
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .192476           .    .192476    .192476
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2725898           .  -.2725898  -.2725898
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0503152           .   .0503152   .0503152
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1318636           .   .1318636   .1318636
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1753296           .   .1753296   .1753296
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.050853           .   -.050853   -.050853
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1481717           .  -.1481717  -.1481717
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1785326           .  -.1785326  -.1785326
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2294644           .  -.2294644  -.2294644
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2458871           .  -.2458871  -.2458871
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.352052           .   -.352052   -.352052
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2801554           .   .2801554   .2801554
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0447339           .   .0447339   .0447339
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3555612           .   .3555612   .3555612
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1      .28201           .     .28201     .28201
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2860673           .   .2860673   .2860673
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1881309           .   .1881309   .1881309
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2366668           .   .2366668   .2366668
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==-1 & !opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0503152           .   .0503152   .0503152

.         gen coef_n = coef-r(mean) if !opo
(31 missing values generated)

. 
.         sum coef if days==-1 & opo

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1247277           .  -.1247277  -.1247277

.         replace coef_n = coef-r(mean) if opo
(30 real changes made)

. 
. 
.         graph twoway (lpoly coef_n days if opo & days<0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if opo & 
> days>0, lp(dash) bwidth(7) color(orange) msize(small)) (lpoly coef_n days if !opo & days<0, bwidth(7) lp(line) color(navy) msize(sma
> ll)) (lpoly coef_n days if !opo & days>0, bwidth(7) lp(line) color(navy) msize(small)) (scatter coef_n days if !opo, msymbol(square)
>  color(navy) msize(small)) (scatter coef_n days if opo & coef_n<.5, msymbol(triangle) msize(small) graphregion(color(white)) legend(
>  cols(1) order(6 "Opposition" 5 "Government")) ytitle("Twitter production", size(large)) xtitle("Days since event", size(large)) lwi
> dth(0.3 0.3) color(orange) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpolyci coef_n days if opo & days<0, bwidth(7) 
> pwidth(7) level(90) lwidth(none) color(orange%20)) (lpolyci coef_n days if opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) 
> color(orange%20)) (lpolyci coef_n days if !opo & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20)) (lpolyci coef_n 
> days if !opo & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(navy%20))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.         graph export "${PathFig}FigureA6.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA6.png written in PNG format)

. 
. 
. restore

. 
. 
. **** FIGURE A11 ****
. 
. 
. reghdfe log_engagement ib1014.event_days_ada_M##mentionOther if timeWindow3_ada & !gg_rt & !gg_adamowicz, abs(userid)
(dropped 4 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      3,818
Absorbing 1 HDFE group                            F(  59,   3677) =      10.65
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5863
                                                  Adj R-squared   =     0.5706
                                                  Within R-sq.    =     0.1459
                                                  Root MSE        =     1.4271

-----------------------------------------------------------------------------------------------
               log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
             event_days_ada_M |
                         985  |  -1.593228   .2182145    -7.30   0.000    -2.021061   -1.165394
                         986  |  -1.465688   .2083996    -7.03   0.000    -1.874278   -1.057097
                         987  |   -.033837    .210737    -0.16   0.872    -.4470099    .3793358
                         988  |   .2575582   .2785156     0.92   0.355    -.2885021    .8036185
                         989  |  -.0223054   .2414523    -0.09   0.926     -.495699    .4510882
                         990  |  -.2771356   .2386541    -1.16   0.246    -.7450431    .1907718
                         991  |  -.0158079   .2161646    -0.07   0.942    -.4396222    .4080065
                         992  |   .0683148   .2081342     0.33   0.743    -.3397551    .4763848
                         993  |  -.2038531   .2283501    -0.89   0.372    -.6515584    .2438522
                         994  |  -.2902322   .2455345    -1.18   0.237    -.7716295    .1911651
                         995  |   .1491873   .2239317     0.67   0.505    -.2898552    .5882298
                         996  |  -.0536889   .2124863    -0.25   0.801    -.4702915    .3629137
                         997  |  -.2753402   .2211869    -1.24   0.213    -.7090013    .1583209
                         998  |    -.17607   .2093666    -0.84   0.400    -.5865561    .2344162
                         999  |   .5001525   .2328708     2.15   0.032     .0435838    .9567212
                        1000  |   .4768103   .1885726     2.53   0.011      .107093    .8465275
                        1001  |   .6810897    .208246     3.27   0.001     .2728008    1.089379
                        1002  |   .5677762    .196076     2.90   0.004     .1833477    .9522047
                        1003  |   .5306613   .2032726     2.61   0.009      .132123    .9291995
                        1004  |   .3461702    .204179     1.70   0.090    -.0541451    .7464855
                        1005  |   .3128489    .206585     1.51   0.130    -.0921836    .7178813
                        1006  |   .5301284    .209842     2.53   0.012     .1187103    .9415465
                        1007  |   .2932365   .1930569     1.52   0.129    -.0852728    .6717457
                        1008  |  -.0404318   .2003735    -0.20   0.840    -.4332861    .3524224
                        1009  |   .4527746   .1927271     2.35   0.019     .0749121    .8306371
                        1010  |   .2847096   .2032216     1.40   0.161    -.1137285    .6831477
                        1011  |   .0275899   .1935992     0.14   0.887    -.3519825    .4071622
                        1012  |   .1423919   .1981775     0.72   0.472    -.2461568    .5309407
                        1013  |   .3457949   .1877211     1.84   0.066    -.0222528    .7138425
                              |
               1.mentionOther |   .7583155   .3364116     2.25   0.024     .0987438    1.417887
                              |
event_days_ada_M#mentionOther |
                       985 1  |   2.513164   .5451791     4.61   0.000     1.444281    3.582047
                       986 1  |   3.633346   .6294815     5.77   0.000     2.399179    4.867514
                       987 1  |   1.008903   .5067473     1.99   0.047     .0153693    2.002437
                       988 1  |   .4998834   .6560244     0.76   0.446    -.7863241    1.786091
                       989 1  |   .6605205   .4619147     1.43   0.153    -.2451138    1.566155
                       990 1  |   .6461386   .4492653     1.44   0.150    -.2346952    1.526972
                       991 1  |   .6244894   .4571684     1.37   0.172    -.2718392    1.520818
                       992 1  |   .8030453   .4776345     1.68   0.093    -.1334094      1.7395
                       993 1  |  -.0585109   .5616034    -0.10   0.917    -1.159596    1.042574
                       994 1  |   .2917831    .491238     0.59   0.553    -.6713428    1.254909
                       995 1  |   .3322058   .4457525     0.75   0.456    -.5417408    1.206152
                       996 1  |   .7300496   .4331364     1.69   0.092    -.1191617    1.579261
                       997 1  |    1.36896    .473539     2.89   0.004     .4405345    2.297385
                       998 1  |   .6088223   .4444398     1.37   0.171    -.2625505    1.480195
                       999 1  |    .230123    .500082     0.46   0.645    -.7503424    1.210588
                      1000 1  |   .0648965    .470472     0.14   0.890    -.8575154    .9873084
                      1001 1  |   .0232758   .6240228     0.04   0.970    -1.200189    1.246741
                      1002 1  |  -.6060267   .4425838    -1.37   0.171    -1.473761    .2617072
                      1003 1  |  -.6129676   .4435756    -1.38   0.167    -1.482646    .2567108
                      1004 1  |  -.1020429    .445418    -0.23   0.819    -.9753336    .7712477
                      1005 1  |    -.68721   .5390288    -1.27   0.202    -1.744035    .3696149
                      1006 1  |  -.8306649   .5385014    -1.54   0.123    -1.886456     .225126
                      1007 1  |  -.9360856   .4244847    -2.21   0.027    -1.768334   -.1038369
                      1008 1  |   .1332527   .5521466     0.24   0.809    -.9492911    1.215797
                      1009 1  |   .1762029   .4467284     0.39   0.693    -.6996569    1.052063
                      1010 1  |  -.1989278   .4728779    -0.42   0.674    -1.126057     .728201
                      1011 1  |   .4841714   .4356726     1.11   0.267    -.3700125    1.338355
                      1012 1  |   .0735336   .4619308     0.16   0.874    -.8321323    .9791994
                      1013 1  |   .9342482   .5352808     1.75   0.081    -.1152283    1.983725
                              |
                        _cons |   2.468231   .1457148    16.94   0.000     2.182541    2.753921
-----------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        82           0          82     |
-----------------------------------------------------+

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>29
(33 observations deleted)

.         /*we drop in 29 instead of 30 because of extra 0*/
.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 30
(1 real change made)

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen menOt = (days>30)

.         replace days = days-30 if menOt
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !menOt
  3.                 replace coef = coef+r(mean) if menOt & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -1.593228           .  -1.593228  -1.593228
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -1.465688           .  -1.465688  -1.465688
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    -.033837           .   -.033837   -.033837
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2575582           .   .2575582   .2575582
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0223054           .  -.0223054  -.0223054
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2771356           .  -.2771356  -.2771356
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0158079           .  -.0158079  -.0158079
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0683148           .   .0683148   .0683148
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2038531           .  -.2038531  -.2038531
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2902322           .  -.2902322  -.2902322
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1491873           .   .1491873   .1491873
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0536889           .  -.0536889  -.0536889
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.2753402           .  -.2753402  -.2753402
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1760699           .  -.1760699  -.1760699
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5001525           .   .5001525   .5001525
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4768102           .   .4768102   .4768102
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .6810898           .   .6810898   .6810898
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5677763           .   .5677763   .5677763
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5306612           .   .5306612   .5306612
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3461702           .   .3461702   .3461702
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3128489           .   .3128489   .3128489
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5301284           .   .5301284   .5301284
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2932365           .   .2932365   .2932365
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0404318           .  -.0404318  -.0404318
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4527746           .   .4527746   .4527746
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2847096           .   .2847096   .2847096
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0275899           .   .0275899   .0275899
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1423919           .   .1423919   .1423919
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3457949           .   .3457949   .3457949
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==-1 & !menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1760699           .  -.1760699  -.1760699

.         gen coef_n = coef-r(mean) if !menOt
(31 missing values generated)

. 
.         sum coef if days==-1 & menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4327523           .   .4327523   .4327523

.         replace coef_n = coef-r(mean) if menOt
(30 real changes made)

. 
.         graph twoway (lpoly coef_n days if menOt & days<0, lp(dash) bwidth(7) color(red) msize(small) graphregion(color(white)) lege
> nd( cols(1) order(1 "Mention rival" 3 "Not mention rival")) ytitle("Tweet engagement", size(large)) xtitle("Days since event", size(
> large)) lwidth(0.3 0.3) tline(0, lp(solid) lc(grey)) graphregion(lwidth(large))) (lpoly coef_n days if menOt & days>0, lp(dash) bwid
> th(7) color(red) msize(small)) (lpoly coef_n days if !menOt & days<0, bwidth(7) lp(line) color(eltgreen) msize(small)) (lpoly coef_n
>  days if !menOt & days>0, bwidth(7) lp(line) color(eltgreen) msize(small)) (lpolyci coef_n days if menOt & days<0, bwidth(7) pwidth(
> 7) level(90) lwidth(none) color(red%20)) (lpolyci coef_n days if menOt & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(re
> d%20)) (lpolyci coef_n days if !menOt & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20)) (lpolyci coef_n days 
> if !menOt & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20)) (scatter coef_n days if !menOt & coef_n>-1 & coef
> _n<1, msymbol(Oh) color(eltgreen) msize(small)) (scatter coef_n days if menOt & coef_n>-1 & coef_n<1, color(red) msymbol(X) msize(me
> dium))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.                 graph export "${PathFig}FigureA11.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA11.png written in PNG format)

. restore

. 
. 
. 
. 
. 
. 
. 
.  *** CORRELATION w/ VOTES ***
. 
. 
.  preserve

.          import excel "${PathData}wybory_pre_post.xlsx", sheet("Sheet1") firstrow clear

.          sort screen_name

.          save "${PathData}wybory_pre_post.dta", replace
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/wybory_pre_post.dta saved

. restore

. 
. sort screen_name

. merge screen_name using "${PathData}wybory_pre_post.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)
variable screen_name does not uniquely identify observations in the master data

. rename _merge mergeVotes

. 
. gen log_votesPost = log(vote_post)
(170,711 missing values generated)

. gen log_votesPre = log(vote_pre)
(164,152 missing values generated)

. gen log_change = log_votesPost - log_votesPre
(170,711 missing values generated)

. gen vote_change = vote_post/vote_pre
(164,152 missing values generated)

. 
. bysort userid: egen meanNGT = mean(mentionOther) if post_ada
(169172 missing values generated)

. bysort userid: egen meanNGpostada = mean(meanNGT)
(375 missing values generated)

. drop meanNGT

. bysort userid: egen meanNGT = mean(mentionOther) if !post_ada
(153048 missing values generated)

. bysort userid: egen meanNGpreada = mean(meanNGT)
(37476 missing values generated)

. drop meanNGT

. gen NGchange = meanNGpostada - meanNGpreada
(37,848 missing values generated)

. 
. bysort userid: egen meanPop_T = mean(log_engagement) if post_ada
(169172 missing values generated)

. bysort userid: egen meanPop_postada = mean(meanPop_T)
(375 missing values generated)

. drop meanPop_T

. bysort userid: egen meanPop_T = mean(log_engagement) if !post_ada
(153048 missing values generated)

. bysort userid: egen meanPop_preada = mean(meanPop_T)
(37476 missing values generated)

. drop meanPop_T

. 
. gen mean_negCamp_diff = meanNGpostada - meanPop_preada
(37,848 missing values generated)

. 
. 
. 
. **** Table A25 ****
. 
. label variable meanNGpostada "Share negative-campaigning tweets post assasination"

. label variable meanNGpreada "Share negative-campaigning tweets pre assasination"

. label variable mean_negCamp_diff "Share negative-campaigning (diff post - pre)"

. label variable log_votesPre "Votes in 2015 (log)"

. eststo clear

. eststo, title("Votes in 2019 (log)"): reg log_votesPost meanNGpostada if n_u==1, robust

Linear regression                               Number of obs     =         39
                                                F(1, 37)          =       2.04
                                                Prob > F          =     0.1620
                                                R-squared         =     0.0606
                                                Root MSE          =     .94388

-------------------------------------------------------------------------------
              |               Robust
log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -2.397161   1.680164    -1.43   0.162    -5.801497    1.007175
        _cons |   10.97446   .4540006    24.17   0.000     10.05457    11.89435
-------------------------------------------------------------------------------
(est1 stored)

. eststo, title("Votes in 2019 (log)"): reg log_votesPost meanNGpostada meanNGpreada if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(2, 35)          =       5.69
                                                Prob > F          =     0.0072
                                                R-squared         =     0.1822
                                                Root MSE          =     .88843

-------------------------------------------------------------------------------
              |               Robust
log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -6.727291   2.144767    -3.14   0.003     -11.0814   -2.373182
 meanNGpreada |    4.07353   1.381136     2.95   0.006     1.269674    6.877385
        _cons |   10.72077   .4548168    23.57   0.000     9.797448     11.6441
-------------------------------------------------------------------------------
(est2 stored)

. eststo, title("Votes in 2019 (log)"): reg log_votesPost meanNGpostada meanNGpreada log_votesPre if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(3, 34)          =       6.50
                                                Prob > F          =     0.0014
                                                R-squared         =     0.2998
                                                Root MSE          =     .83409

-------------------------------------------------------------------------------
              |               Robust
log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -5.159156   1.948555    -2.65   0.012    -9.119096   -1.199217
 meanNGpreada |   3.608981   1.333202     2.71   0.011     .8995895    6.318373
 log_votesPre |    .524661   .1995914     2.63   0.013     .1190424    .9302795
        _cons |   5.290115    1.98692     2.66   0.012     1.252209    9.328022
-------------------------------------------------------------------------------
(est3 stored)

. eststo, title("Votes in 2019 (log)"): reg log_votesPost mean_negCamp_diff log_votesPre if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(2, 35)          =      10.35
                                                Prob > F          =     0.0003
                                                R-squared         =     0.2882
                                                Root MSE          =     .82883

-----------------------------------------------------------------------------------
                  |               Robust
    log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
mean_negCamp_diff |   -.307533   .1188794    -2.59   0.014    -.5488711   -.0661949
     log_votesPre |    .602227   .2060019     2.92   0.006      .184021    1.020433
            _cons |   3.516025   1.978235     1.78   0.084    -.5000059    7.532056
-----------------------------------------------------------------------------------
(est4 stored)

. eststo, title("Votes in 2015 (log)"): reg log_votesPre meanNGpostada meanNGpreada  if n_u==1, robust

Linear regression                               Number of obs     =         40
                                                F(2, 37)          =       2.03
                                                Prob > F          =     0.1456
                                                R-squared         =     0.1033
                                                Root MSE          =     .63087

-------------------------------------------------------------------------------
              |               Robust
 log_votesPre |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -2.892635    2.15211    -1.34   0.187    -7.253225    1.467955
 meanNGpreada |   .9377048   1.655597     0.57   0.575    -2.416854    4.292264
        _cons |    10.3246   .2699793    38.24   0.000     9.777565    10.87163
-------------------------------------------------------------------------------
(est5 stored)

. eststo, title("Votes in 2015 (log)"): reg log_votesPre mean_negCamp_diff  if n_u==1, robust

Linear regression                               Number of obs     =         40
                                                F(1, 38)          =       0.23
                                                Prob > F          =     0.6330
                                                R-squared         =     0.0058
                                                Root MSE          =     .65548

-----------------------------------------------------------------------------------
                  |               Robust
     log_votesPre |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
mean_negCamp_diff |  -.0502351   .1043669    -0.48   0.633    -.2615148    .1610446
            _cons |   9.770541    .311658    31.35   0.000     9.139623    10.40146
-----------------------------------------------------------------------------------
(est6 stored)

. esttab using "${PathTab}TableA25.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(meanNGpostada meanNGpreada mean_negCamp_diff log
> _votesPre) order(meanNGpostada meanNGpreada mean_negCamp_diff log_votesPre) stats(N, fmt(0) labels("N")) label nodepvar mtitles titl
> e("Negative campaiging and electoral outcomes") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA25.tex)

. 
. 
. 
. **** Figure 6 ****
. 
. 
. set scheme plotplainblind

. 
. reg log_votesPost meanNGpostada meanNGpreada log_votesPre if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(3, 34)          =       6.50
                                                Prob > F          =     0.0014
                                                R-squared         =     0.2998
                                                Root MSE          =     .83409

-------------------------------------------------------------------------------
              |               Robust
log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -5.159156   1.948555    -2.65   0.012    -9.119096   -1.199217
 meanNGpreada |   3.608981   1.333202     2.71   0.011     .8995895    6.318373
 log_votesPre |    .524661   .1995914     2.63   0.013     .1190424    .9302795
        _cons |   5.290115    1.98692     2.66   0.012     1.252209    9.328022
-------------------------------------------------------------------------------

. avplot meanNGpostada, mcolor(red) ytitle(Votes in 2019 (log)) scale(1.3) xtitle(Share of negative-campaigning tweets {bf:post} assas
> ination) graphregion(color(white))

. graph export "${PathFig}Figure6a.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure6a.png written in PNG format)

. 
. reg log_votesPost meanNGpostada meanNGpreada log_votesPre if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(3, 34)          =       6.50
                                                Prob > F          =     0.0014
                                                R-squared         =     0.2998
                                                Root MSE          =     .83409

-------------------------------------------------------------------------------
              |               Robust
log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
meanNGpostada |  -5.159156   1.948555    -2.65   0.012    -9.119096   -1.199217
 meanNGpreada |   3.608981   1.333202     2.71   0.011     .8995895    6.318373
 log_votesPre |    .524661   .1995914     2.63   0.013     .1190424    .9302795
        _cons |   5.290115    1.98692     2.66   0.012     1.252209    9.328022
-------------------------------------------------------------------------------

. avplot meanNGpreada, mcolor(red*0.5) ytitle(Votes in 2019 (log)) scale(1.3) xtitle(Share of negative-campaigning tweets {bf:pre} ass
> asination) graphregion(color(white))

. graph export "${PathFig}Figure6b.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure6b.png written in PNG format)

. 
. reg log_votesPost mean_negCamp_diff log_votesPre if n_u==1, robust

Linear regression                               Number of obs     =         38
                                                F(2, 35)          =      10.35
                                                Prob > F          =     0.0003
                                                R-squared         =     0.2882
                                                Root MSE          =     .82883

-----------------------------------------------------------------------------------
                  |               Robust
    log_votesPost |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
mean_negCamp_diff |   -.307533   .1188794    -2.59   0.014    -.5488711   -.0661949
     log_votesPre |    .602227   .2060019     2.92   0.006      .184021    1.020433
            _cons |   3.516025   1.978235     1.78   0.084    -.5000059    7.532056
-----------------------------------------------------------------------------------

. avplot mean_negCamp_diff, mcolor(red*0.5) ytitle(Votes in 2019 (log)) scale(1.3) aspectratio(0.9) xtitle(Share negative-campaigning 
> (difference post - pre assasination)) graphregion(color(white))

. graph export "${PathFig}Figure6c.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure6c.png written in PNG format)

. 
end of do-file

. do "2_twitter_sentiment.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All tables and graphs relying on sentiment analysis
. 
. ********************************************************************************
. 
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. * Path 
. 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}tweets_final.dta", clear

. 
. 
. 
. sort id

. merge id using "${PathData}tweets_sentiment_coded_300.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)

. 
. 
. 
. ***** Coding VARIABLES (general) *****
. 
. gen dOfWeek = mod(date2,7)

. replace dOfWeek = dOfWeek - 2
(239,481 real changes made)

. replace dOfWeek = dOfWeek + 7 if dOfWeek<1
(101,594 real changes made)

. 
. drop gg_rt

. gen gg_rt = strpos(text, "rt ") == 1

. 
. gen gg_adamowicz = strpos(text, "adamowicz")>0

. 
. gen log_rt = log(rt_count + 1)

. gen log_fav = log(favcount + 1)

. gen log_engagement = log(rt_count + favcount + 1)

. egen userid = group(screen_name)

. bysort date2 userid: gen N_du = _N

. 
. 
. 
. **** Coding TREATMENT (Adamowicz) *******
. 
.         
. gen dayC_ada = date2 - 21562

. gen dayC_sq_ada = dayC_ada*dayC_ada

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada

. gen post_ada = date2>=21562

. gen dayC_post_ada = dayC_ada*post_ada

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada

. gen dayC_opos_ada = dayC_ada*opposition

. gen dayC_sq_opos_ada = dayC_sq_ada*opposition

. gen dayC_cu_opos_ada = dayC_cu_ada*opposition

. gen dayC_opos_post_ada = dayC_ada*opposition*post_ada

. gen dayC_sq_opos_post_ada = dayC_sq_ada*opposition*post_ada

. gen dayC_cu_opos_post_ada = dayC_cu_ada*opposition*post_ada

. gen post_opo_ada = post_ada*opposition

. gen post_gob_ada = post_ada*government

. 
. gen hour_sq = hour*hour

. gen hour_cu = hour*hour*hour

. 
. bysort userid: gen n_u = _n

. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21562

. foreach targetdate in 21562 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(239,481 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21562

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
. 
. 
. 
. 
. **** Coding OTHER MENTIONS *******
. 
. 
. replace text = " " + text
(239,481 real changes made)

. gen opMentions = strpos(text," rzd") + strpos(text," min ") + strpos(text,"minist") + strpos(text,"wicemin") + strpos(text,"pis ") +
>  strpos(text,"tvp") + strpos(text,"andruszkiewicz") + strpos(text,"berni_krynick") + strpos(text,"budet") + strpos(text,"premier") +
>  strpos(text,"morawieck") + strpos(text,"pisowsk") + strpos(text,"macierewicz") + strpos(text,"kaczysk") + strpos(text," msz ") + st
> rpos(text,"tygodnik_sieci") + strpos(text,"smolesk") + strpos(text,"misiewicz") + strpos(text,"tasmykaczynsk") + strpos(text,"glapis
> ki") + strpos(text,"glapinski") + strpos(text,"szydo") + strpos(text,"ziobro") + strpos(text,"zieliski") + strpos(text,"gowin") + st
> rpos(text,"jbrudzinski") + strpos(text,"drelich") + strpos(text,"terlecki") + strpos(text,"sdownictw") + strpos(text,"pisorgpl") + s
> trpos(text," nbp ") + strpos(text,"wicemarsz") + strpos(text," cba ") + strpos(text,"patryk jaki") + strpos(text,"policj") + strpos(
> text,"czaputowicz") + strpos(text,"gliski")

. replace opMentions = opMentions>0
(78,190 real changes made)

. gen govMentions = strpos(text," opozycj") + strpos(text,"bzdrojewski") + strpos(text," lewic") + strpos(text,"olejnik") + strpos(tex
> t," lewicow") + strpos(text," biedro") + strpos(text," lewac") + strpos(text," psl_") + strpos(text," psl ") + strpos(text,"gazwyb")
>  + strpos(text," nowick") + strpos(text," sikorsk") + strpos(text,"schetyn") + strpos(text," neuman") + strpos(text,"tomasz_lis") + 
> strpos(text,"tomasz lis") + strpos(text," lis_tomasz") + strpos(text," tusk") + strpos(text," platform") + strpos(text,"trzaskowski_
> ") + strpos(text,"platforma_org") + strpos(text,"klubnauer") + strpos(text," gw_") + strpos(text,"gazetawyborcza") + strpos(text,"tv
> n24")

. replace govMentions = govMentions>0
(28,793 real changes made)

. replace govMentions = govMentions + strpos(text,"platforma_org")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(2,878 real changes made)

. replace govMentions = govMentions + strpos(text,"arlukowicz")>0
(1,456 real changes made)

. replace govMentions = govMentions + strpos(text,"bbudka")>0
(1,107 real changes made)

. replace govMentions = govMentions + strpos(text,"andrzejhalicki")>0
(964 real changes made)

. replace govMentions = govMentions + strpos(text,"slawekneumann")>0
(1,439 real changes made)

. replace govMentions = govMentions + strpos(text,"schetynadlapo")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mkierwinski")>0
(1,378 real changes made)

. replace govMentions = govMentions + strpos(text,"jangrabiec")>0
(1,137 real changes made)

. replace govMentions = govMentions + strpos(text,"achybicka")>0
(142 real changes made)

. replace govMentions = govMentions + strpos(text,"protasiewiczj")>0
(122 real changes made)

. replace govMentions = govMentions + strpos(text,"zpawlowicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"trzaskowski_")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszlenz")>0
(79 real changes made)

. replace govMentions = govMentions + strpos(text,"hannagw")>0
(316 real changes made)

. replace govMentions = govMentions + strpos(text,"asia_mucha")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"niesiolowskis")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"bbukiewicz")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"c_grabarczyk")>0
(11 real changes made)

. replace govMentions = govMentions + strpos(text,"ikatarasinska")>0
(91 real changes made)

. replace govMentions = govMentions + strpos(text,"ctomczyk")>0
(800 real changes made)

. replace govMentions = govMentions + strpos(text,"sowamarek")>0
(196 real changes made)

. replace govMentions = govMentions + strpos(text,"ireneuszras")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"kopacz_ewa")>0
(31 real changes made)

. replace govMentions = govMentions + strpos(text,"m_k_blonska")>0
(449 real changes made)

. replace govMentions = govMentions + strpos(text,"adam_korol")>0
(166 real changes made)

. replace govMentions = govMentions + strpos(text,"pomaska")>0
(776 real changes made)

. replace govMentions = govMentions + strpos(text,"henryka_henia50")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"miroslawanykiel")>0
(81 real changes made)

. replace govMentions = govMentions + strpos(text,"marianzembala")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"urszulaaugustyn")>0
(205 real changes made)

. replace govMentions = govMentions + strpos(text,"kr_szumilas")>0
(327 real changes made)

. replace govMentions = govMentions + strpos(text,"okladrewnowicz")>0
(317 real changes made)

. replace govMentions = govMentions + strpos(text,"mswitczak")>0
(632 real changes made)

. replace govMentions = govMentions + strpos(text,"jakubrutnicki")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"mjanyska")>0
(219 real changes made)

. replace govMentions = govMentions + strpos(text,"ziolkowskiszym")>0
(105 real changes made)

. replace govMentions = govMentions + strpos(text,"waldydzikowski")>0
(105 real changes made)

. replace govMentions = govMentions + strpos(text,"slawomirnitras")>0
(753 real changes made)

. replace govMentions = govMentions + strpos(text,"krzysztofbrejza")>0
(1,311 real changes made)

. replace govMentions = govMentions + strpos(text,"arkadiuszmyrcha")>0
(823 real changes made)

. replace govMentions = govMentions + strpos(text,"mwielichowska")>0
(887 real changes made)

. replace govMentions = govMentions + strpos(text,"gajewska_kinga")>0
(305 real changes made)

. replace govMentions = govMentions + strpos(text,"newsplatforma")>0
(2,006 real changes made)

. replace govMentions = govMentions + strpos(text,"grzegorzfurgo")>0
(207 real changes made)

. replace govMentions = govMentions + strpos(text,"bborusewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"rtyszkiewicz")>0
(579 real changes made)

. replace govMentions = govMentions + strpos(text,"jaroslawduda")>0
(184 real changes made)

. replace govMentions = govMentions + strpos(text,"zdzislaw_gawlik")>0
(53 real changes made)

. replace govMentions = govMentions + strpos(text,"arturgierada")>0
(14 real changes made)

. replace govMentions = govMentions + strpos(text,"a_marchewka")>0
(114 real changes made)

. replace govMentions = govMentions + strpos(text,"jacek_protas")>0
(108 real changes made)

. replace govMentions = govMentions + strpos(text,"wojciechsaluga")>0
(189 real changes made)

. replace govMentions = govMentions + strpos(text,"wslugocki")>0
(424 real changes made)

. replace govMentions = govMentions + strpos(text,"hannazdanowska")>0
(89 real changes made)

. replace govMentions = govMentions + strpos(text,"prezydentzuk")>0
(15 real changes made)

. replace govMentions = govMentions + strpos(text,"izabela_debska")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"tadeuszzwiefka")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"donaldtusk")>0
(502 real changes made)

. replace opMentions = opMentions + strpos(text,"piotr_naimski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"pisorgpl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"morawieckim")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jbrudzinski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"patrykjaki")>0
(918 real changes made)

. replace opMentions = opMentions + strpos(text,"andzyberto")>0
(652 real changes made)

. replace opMentions = opMentions + strpos(text,"kurskipl")>0
(127 real changes made)

. replace opMentions = opMentions + strpos(text,"piotrglinski")>0
(414 real changes made)

. replace opMentions = opMentions + strpos(text,"jaroslaw_gowin")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"stkarczewski")>0
(290 real changes made)

. replace opMentions = opMentions + strpos(text,"marekkuchcinski")>0
(447 real changes made)

. replace opMentions = opMentions + strpos(text,"beataszydlo")>0
(586 real changes made)

. replace opMentions = opMentions + strpos(text,"beatamk")>0
(702 real changes made)

. replace opMentions = opMentions + strpos(text,"e_rafalska")>0
(164 real changes made)

. replace opMentions = opMentions + strpos(text,"krystpawlowicz")>0
(108 real changes made)

. replace opMentions = opMentions + strpos(text,"ziobropl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"beatakempa_kprm")>0
(165 real changes made)

. replace opMentions = opMentions + strpos(text,"mblaszczak")>0
(382 real changes made)

. replace opMentions = opMentions + strpos(text,"macierewicz_a")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"elzbietawitek")>0
(8 real changes made)

. replace opMentions = opMentions + strpos(text,"_annazalewska")>0
(104 real changes made)

. replace opMentions = opMentions + strpos(text,"michaldworczyk")>0
(220 real changes made)

. replace opMentions = opMentions + strpos(text,"latostomasz")>0
(10 real changes made)

. replace opMentions = opMentions + strpos(text,"slawzawislak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"wassermann_ma")>0
(230 real changes made)

. replace opMentions = opMentions + strpos(text,"arekmularczyk")>0
(463 real changes made)

. replace opMentions = opMentions + strpos(text,"w_bernacki")>0
(37 real changes made)

. replace opMentions = opMentions + strpos(text,"mareksuski")>0
(37 real changes made)

. replace opMentions = opMentions + strpos(text,"akosztowniak")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"minenergii")>0
(150 real changes made)

. replace opMentions = opMentions + strpos(text,"a_czartoryski")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"mariuszkaminsk")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"sasinjacek ?")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"bogdan_rzonca")>0
(17 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejszlachta")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mrirw_gov_pl")>0
(125 real changes made)

. replace opMentions = opMentions + strpos(text,"d_piontkowski")>0
(22 real changes made)

. replace opMentions = opMentions + strpos(text,"mkidn_gov_pl")>0
(338 real changes made)

. replace opMentions = opMentions + strpos(text,"stanislaw_szwed")>0
(133 real changes made)

. replace opMentions = opMentions + strpos(text,"szymongizynski")>0
(4 real changes made)

. replace opMentions = opMentions + strpos(text,"izabelakloc")>0
(37 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejduda")>0
(1,613 real changes made)

. replace opMentions = opMentions + strpos(text,"annamkrupka")>0
(58 real changes made)

. replace opMentions = opMentions + strpos(text,"iwonaarent")>0
(10 real changes made)

. replace opMentions = opMentions + strpos(text,"joannalichocka")>0
(105 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldczarneck3")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"tadeuszdziuba")>0
(7 real changes made)

. replace opMentions = opMentions + strpos(text,"zagorskimarek")>0
(100 real changes made)

. replace opMentions = opMentions + strpos(text,"marekgrobarczyk")>0
(32 real changes made)

. replace opMentions = opMentions + strpos(text,"amadamczyk")>0
(528 real changes made)

. replace opMentions = opMentions + strpos(text,"jerzykwiecinski")>0
(354 real changes made)

. replace opMentions = opMentions + strpos(text,"jemilewicz")>0
(202 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldbanka")>0
(179 real changes made)

. replace opMentions = opMentions + strpos(text,"jczaputowicz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szumowskilukasz")>0
(48 real changes made)

. replace opMentions = opMentions + strpos(text,"j_kopcinska")>0
(53 real changes made)

. replace opMentions = opMentions + strpos(text,"profkarski")>0
(8 real changes made)

. replace opMentions = opMentions + strpos(text,"ryszardterlecki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"r_czarnecki")>0
(362 real changes made)

. replace opMentions = opMentions + strpos(text,"grzegorzczelej")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"anita_cz")>0
(38 real changes made)

. replace opMentions = opMentions + strpos(text,"ministerjurgiel")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"cymanskitadeusz")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"jacekzalek")>0
(80 real changes made)

. gen mentionOther = (opMentions & opposition) | (govMentions & government)

. replace mentionOther = 0 if mentionOther == .
(0 real changes made)

. 
. 
. **** Coding other VARIABLES *******
. 
. 
. gen post_mention_ada = post_ada*mentionOther

. gen post_opo_mention_ada = post_ada*opposition*mentionOther

. gen opo_mention = opposition*mentionOther

. 
. gen mention_ada = mentionOther*gg_adamowicz

. gen opo_ada = opposition*gg_adamowicz

. gen mention_opo_ada= mentionOther*gg_adamowicz*opposition

. 
. 
. 
. drop n_u

. bysort userid: gen n_u=_n

. bysort userid: gen N_u=_N

. drop if N_u<10
(5 observations deleted)

. reg log_engagement i.userid if !post_ada

      Source |       SS           df       MS      Number of obs   =   169,020
-------------+----------------------------------   F(102, 168917)  =    810.66
       Model |  179184.824       102  1756.71396   Prob > F        =    0.0000
    Residual |  366045.395   168,917  2.16701336   R-squared       =    0.3286
-------------+----------------------------------   Adj R-squared   =    0.3282
       Total |   545230.22   169,019  3.22585165   Root MSE        =    1.4721

------------------------------------------------------------------------------
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      userid |
          2  |  -2.133226   .0874477   -24.39   0.000    -2.304621    -1.96183
          3  |  -.7488332   .0698943   -10.71   0.000    -.8858245   -.6118419
          4  |  -.4453191   .0771017    -5.78   0.000    -.5964367   -.2942015
          5  |  -.3548494   .0694986    -5.11   0.000    -.4910651   -.2186336
          6  |   .7447036   .0689024    10.81   0.000     .6096564    .8797507
          7  |  -.2591751   .0703109    -3.69   0.000    -.3969829   -.1213672
          8  |    .324888   .0696579     4.66   0.000       .18836    .4614159
          9  |  -.3027277   .0712688    -4.25   0.000    -.4424131   -.1630424
         10  |   .7653459   .0743287    10.30   0.000     .6196632    .9110286
         11  |   .6125188    .073223     8.37   0.000     .4690033    .7560342
         12  |   .0748455   .0828517     0.90   0.366     -.087542    .2372331
         13  |   .9617988   .0740016    13.00   0.000     .8167573     1.10684
         14  |  -.7621935   .0713943   -10.68   0.000    -.9021248   -.6222623
         15  |   1.425522   .0815246    17.49   0.000     1.265735    1.585308
         16  |   .8111858    .087621     9.26   0.000     .6394505    .9829211
         17  |   .2661439   .0771235     3.45   0.001     .1149835    .4173044
         18  |   .9872882   .0693634    14.23   0.000     .8513375    1.123239
         19  |  -2.766344   .2853572    -9.69   0.000    -3.325638    -2.20705
         20  |  -.9327522   .0743772   -12.54   0.000     -1.07853   -.7869746
         21  |  -1.168859   .0815633   -14.33   0.000    -1.328722   -1.008997
         23  |  -.2505409   .0696271    -3.60   0.000    -.3870086   -.1140733
         24  |   1.318015   .0690651    19.08   0.000     1.182649    1.453381
         25  |   1.370812   .0705231    19.44   0.000     1.232588    1.509035
         26  |   .7420353   .0697294    10.64   0.000     .6053672    .8787035
         27  |  -.1116604   .0707459    -1.58   0.114    -.2503209    .0270001
         28  |  -.5695629   .0705578    -8.07   0.000    -.7078547   -.4312711
         29  |   .7705009   .0723784    10.65   0.000     .6286408     .912361
         31  |   1.469057    .096214    15.27   0.000      1.28048    1.657634
         32  |   .4432172   .0788803     5.62   0.000     .2886135    .5978208
         33  |  -1.325784   .0847382   -15.65   0.000    -1.491869   -1.159699
         34  |  -.8874022    .070503   -12.59   0.000    -1.025587   -.7492178
         35  |  -.8398518   .0704378   -11.92   0.000    -.9779083   -.7017953
         36  |   .7899041   .0717325    11.01   0.000     .6493099    .9304983
         37  |  -1.396041   .0699087   -19.97   0.000    -1.533061   -1.259022
         38  |   .4067855   .0708265     5.74   0.000      .267967     .545604
         39  |   .7112572   .0694037    10.25   0.000     .5752275     .847287
         40  |   1.257011   .0695558    18.07   0.000     1.120684    1.393339
         41  |   1.416453   .0703959    20.12   0.000     1.278479    1.554427
         42  |   -.171936   .3527393    -0.49   0.626    -.8632973    .5194254
         43  |  -.8834176   .0689694   -12.81   0.000    -1.018596    -.748239
         44  |  -1.617421   .0689177   -23.47   0.000    -1.752499   -1.482344
         45  |   4.258477   .1065664    39.96   0.000     4.049609    4.467345
         47  |   .5524788   .1960419     2.82   0.005     .1682408    .9367167
         48  |   .2348751    .069601     3.37   0.001     .0984587    .3712915
         49  |   2.496325   .0691679    36.09   0.000     2.360757    2.631892
         50  |  -.0212228   .0731789    -0.29   0.772    -.1646518    .1222062
         52  |  -.4733122   .0690941    -6.85   0.000    -.6087351   -.3378893
         53  |    1.14983   .0703848    16.34   0.000     1.011877    1.287782
         54  |   .6755264   .0895892     7.54   0.000     .4999336    .8511193
         55  |   1.804398   .0729694    24.73   0.000      1.66138    1.947417
         56  |   1.902092   .3734618     5.09   0.000     1.170116    2.634069
         57  |   .9994903   .0719066    13.90   0.000     .8585549    1.140426
         58  |   1.575096   .0795521    19.80   0.000     1.419176    1.731017
         59  |   .2920576   .0700382     4.17   0.000     .1547844    .4293309
         60  |   .7075415   .0690543    10.25   0.000     .5721966    .8428864
         61  |  -.4965199   .0690814    -7.19   0.000    -.6319178   -.3611219
         62  |   -.556105   .1109344    -5.01   0.000    -.7735341    -.338676
         63  |  -.8691236   .1037558    -8.38   0.000    -1.072483   -.6657646
         64  |  -1.068274   .0764084   -13.98   0.000    -1.218033   -.9185155
         65  |   -1.53358   .2217691    -6.92   0.000    -1.968242   -1.098917
         66  |   .2330107   .0696381     3.35   0.001     .0965216    .3694998
         67  |   .3959812   .1289741     3.07   0.002     .1431948    .6487676
         68  |   .1091955   .0784192     1.39   0.164    -.0445043    .2628954
         69  |  -.1070819   .0687634    -1.56   0.119    -.2418567    .0276929
         70  |   -.449776   .0836168    -5.38   0.000    -.6136631   -.2858889
         71  |  -.1917351   .0697704    -2.75   0.006    -.3284836   -.0549866
         72  |   1.247415   .3436404     3.63   0.000     .5738868    1.920942
         73  |  -.8157982   .0688106   -11.86   0.000    -.9506654    -.680931
         74  |  -.0835372   .0719775    -1.16   0.246    -.2246115    .0575372
         75  |  -.1299642   .1000257    -1.30   0.194    -.3260124    .0660839
         76  |   .7381418   .0805233     9.17   0.000     .5803178    .8959658
         77  |  -1.162235   .0718942   -16.17   0.000    -1.303146   -1.021324
         78  |  -.9251694   .1289741    -7.17   0.000    -1.177956    -.672383
         79  |  -.2526829   .1736203    -1.46   0.146    -.5929748     .087609
         80  |   .9370455   .0713114    13.14   0.000     .7972767    1.076814
         81  |   .4783238   .1617028     2.96   0.003     .1613899    .7952577
         82  |     .85705    .069457    12.34   0.000     .7209158    .9931841
         83  |  -1.758092   .0688336   -25.54   0.000    -1.893004    -1.62318
         86  |  -2.258829   .0686299   -32.91   0.000    -2.393343   -2.124316
         87  |   1.812503   .0699399    25.92   0.000     1.675422    1.949584
         88  |   1.103261   .1219446     9.05   0.000     .8642522    1.342269
         89  |   2.355238   .0904808    26.03   0.000     2.177897    2.532578
         90  |   .8861239   .0935765     9.47   0.000     .7027159    1.069532
         91  |   .7532127   .0695837    10.82   0.000     .6168302    .8895951
         92  |   .6511821   .0687506     9.47   0.000     .5164325    .7859317
         94  |  -.9153809   .0697476   -13.12   0.000    -1.052085   -.7786772
         95  |    .541038   .0710662     7.61   0.000     .4017499    .6803262
         96  |     .07554   .0744824     1.01   0.310    -.0704438    .2215239
         97  |   .7738392   .0740865    10.45   0.000     .6286314     .919047
         98  |   1.483318    .307128     4.83   0.000     .8813533    2.085282
         99  |   .2129311   .0712901     2.99   0.003     .0732042    .3526581
        100  |  -1.483282   .0740996   -20.02   0.000    -1.628516   -1.338049
        101  |    .053478   .1158293     0.46   0.644    -.1735448    .2805008
        102  |   .6453393   .0702044     9.19   0.000     .5077403    .7829383
        103  |   .5387714   .0708172     7.61   0.000     .3999713    .6775714
        104  |  -2.623314   .3527393    -7.44   0.000    -3.314675   -1.931953
        105  |   1.384683    .072543    19.09   0.000       1.2425    1.526866
        107  |   1.190608   .0889494    13.39   0.000     1.016269    1.364947
        108  |   -1.62155   .2853572    -5.68   0.000    -2.180844   -1.062256
        109  |   .2476323   .0715315     3.46   0.001     .1074322    .3878324
        110  |  -1.862488   .0688336   -27.06   0.000    -1.997401   -1.727576
        111  |   1.244117   .0824588    15.09   0.000       1.0825    1.405734
             |
       _cons |   2.815855   .0635249    44.33   0.000     2.691348    2.940362
------------------------------------------------------------------------------

. predict popularity
(option xb assumed; fitted values)

. egen popRankT = rank(-popularity) if n_u==1 & opposition, unique
(239427 missing values generated)

. bysort userid: egen popRankO = mean(popRankT)
(113850 missing values generated)

. drop popRankT

. egen popRankT = rank(-popularity) if n_u==1 & government, unique
(239416 missing values generated)

. bysort userid: egen popRankG = mean(popRankT)
(125626 missing values generated)

. drop popRankT

. gen popRankWithin = popRankG if government
(125,626 missing values generated)

. replace popRankWithin = popRankO if opposition
(125,626 real changes made)

. egen popRankT = rank(-popularity) if n_u==1, unique
(239367 missing values generated)

. bysort userid: egen popRank = mean(popRankT)

. drop popRankT

. 
. reg mentionOther i.userid if !post_ada

      Source |       SS           df       MS      Number of obs   =   169,020
-------------+----------------------------------   F(102, 168917)  =    316.87
       Model |  3601.57939       102  35.3096019   Prob > F        =    0.0000
    Residual |  18823.1329   168,917  .111434212   R-squared       =    0.1606
-------------+----------------------------------   Adj R-squared   =    0.1601
       Total |  22424.7122   169,019  .132675689   Root MSE        =    .33382

------------------------------------------------------------------------------
mentionOther |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      userid |
          2  |  -.0633054   .0198302    -3.19   0.001    -.1021721   -.0244387
          3  |  -.0914427   .0158497    -5.77   0.000    -.1225077   -.0603776
          4  |   .1790367   .0174841    10.24   0.000     .1447684    .2133051
          5  |  -.0271319   .0157599    -1.72   0.085    -.0580211    .0037572
          6  |  -.0456786   .0156247    -2.92   0.003    -.0763027   -.0150545
          7  |   .0416583   .0159441     2.61   0.009     .0104081    .0729085
          8  |   -.045594   .0157961    -2.89   0.004    -.0765539   -.0146341
          9  |   .0004037   .0161614     0.02   0.980    -.0312723    .0320796
         10  |   .3029661   .0168553    17.97   0.000     .2699301     .336002
         11  |   .1651259   .0166045     9.94   0.000     .1325814    .1976703
         12  |  -.0531966    .018788    -2.83   0.005    -.0900206   -.0163726
         13  |  -.0484295   .0167811    -2.89   0.004      -.08132    -.015539
         14  |  -.0004106   .0161898    -0.03   0.980    -.0321423    .0313211
         15  |   .3737026    .018487    20.21   0.000     .3374684    .4099368
         16  |  -.0042158   .0198695    -0.21   0.832    -.0431595     .034728
         17  |  -.0826155    .017489    -4.72   0.000    -.1168936   -.0483374
         18  |   .2702266   .0157293    17.18   0.000     .2393976    .3010557
         19  |  -.0592578   .0647094    -0.92   0.360    -.1860868    .0675712
         20  |  -.0148616   .0168662    -0.88   0.378     -.047919    .0181959
         21  |  -.0213006   .0184958    -1.15   0.249    -.0575519    .0149508
         23  |   -.087473   .0157891    -5.54   0.000    -.1184193   -.0565268
         24  |  -.0597178   .0156616    -3.81   0.000    -.0904143   -.0290214
         25  |   .4136859   .0159923    25.87   0.000     .3823415    .4450304
         26  |   .2663404   .0158123    16.84   0.000     .2353487    .2973321
         27  |  -.0717058   .0160428    -4.47   0.000    -.1031494   -.0402623
         28  |  -.0832227   .0160001    -5.20   0.000    -.1145826   -.0518628
         29  |   .0188536    .016413     1.15   0.251    -.0133155    .0510227
         31  |    .324305   .0218181    14.86   0.000     .2815421     .367068
         32  |  -.0011275   .0178874    -0.06   0.950    -.0361864    .0339314
         33  |   .0298465   .0192158     1.55   0.120     -.007816     .067509
         34  |   .0793913   .0159877     4.97   0.000     .0480558    .1107269
         35  |  -.0919806   .0159729    -5.76   0.000    -.1232872   -.0606741
         36  |   .3450894   .0162665    21.21   0.000     .3132074    .3769714
         37  |  -.0859312   .0158529    -5.42   0.000    -.1170026   -.0548598
         38  |   .2146703   .0160611    13.37   0.000      .183191    .2461497
         39  |   .0831743   .0157384     5.28   0.000     .0523274    .1140213
         40  |  -.0716387   .0157729    -4.54   0.000    -.1025533   -.0407242
         41  |  -.0839317   .0159634    -5.26   0.000    -.1152197   -.0526438
         42  |  -.0394165   .0799894    -0.49   0.622     -.196194    .1173609
         43  |  -.0916432   .0156399    -5.86   0.000    -.1222971   -.0609892
         44  |   .1153114   .0156282     7.38   0.000     .0846804    .1459423
         45  |  -.0882153   .0241657    -3.65   0.000    -.1355795   -.0408511
         47  |   .4447105   .0444557    10.00   0.000     .3575783    .5318427
         48  |   .1525083   .0157832     9.66   0.000     .1215737     .183443
         49  |  -.0154973    .015685    -0.99   0.323    -.0462395    .0152449
         50  |  -.0724386   .0165945    -4.37   0.000    -.1049634   -.0399137
         52  |   .1197519   .0156682     7.64   0.000     .0890425    .1504612
         53  |    .265774   .0159609    16.65   0.000      .234491     .297057
         54  |   .0320998   .0203158     1.58   0.114    -.0077188    .0719183
         55  |   .1655871    .016547    10.01   0.000     .1331554    .1980189
         56  |   .0300279   .0846886     0.35   0.723    -.1359598    .1960156
         57  |   .2973799    .016306    18.24   0.000     .2654206    .3293393
         58  |   .3642872   .0180397    20.19   0.000     .3289297    .3996447
         59  |   .3056702   .0158823    19.25   0.000     .2745413    .3367992
         60  |  -.0388151   .0156592    -2.48   0.013    -.0695068   -.0081234
         61  |  -.0718507   .0156653    -4.59   0.000    -.1025544    -.041147
         62  |  -.0797049   .0251562    -3.17   0.002    -.1290105   -.0303993
         63  |   .0323571   .0235283     1.38   0.169    -.0137578    .0784721
         64  |  -.0691817   .0173269    -3.99   0.000     -.103142   -.0352215
         65  |   .0508613   .0502897     1.01   0.312    -.0477055    .1494281
         66  |   .2115644   .0157916    13.40   0.000     .1806132    .2425155
         67  |    .399214    .029247    13.65   0.000     .3418905    .4565374
         68  |    .247467   .0177828    13.92   0.000      .212613    .2823209
         69  |   .1368795   .0155932     8.78   0.000     .1063172    .1674419
         70  |  -.0172094   .0189615    -0.91   0.364    -.0543735    .0199546
         71  |  -.0853678   .0158216    -5.40   0.000    -.1163777   -.0543578
         72  |  -.0423405   .0779261    -0.54   0.587    -.1950739    .1103929
         73  |   .0570615   .0156039     3.66   0.000     .0264782    .0876448
         74  |  -.0600883   .0163221    -3.68   0.000    -.0920793   -.0280974
         75  |   .2135679   .0226825     9.42   0.000     .1691108     .258025
         76  |  -.0644636     .01826    -3.53   0.000    -.1002528   -.0286744
         77  |   .1644422   .0163032    10.09   0.000     .1324883    .1963961
         78  |  -.0193907    .029247    -0.66   0.507    -.0767141    .0379328
         79  |  -.0467793   .0393712    -1.19   0.235     -.123946    .0303874
         80  |    .329931    .016171    20.40   0.000     .2982362    .3616259
         81  |  -.0337476   .0366687    -0.92   0.357    -.1056175    .0381223
         82  |  -.0643933   .0157505    -4.09   0.000    -.0952639   -.0335226
         83  |   .1203327   .0156091     7.71   0.000     .0897391    .1509263
         86  |   .0354763    .015563     2.28   0.023     .0049732    .0659793
         87  |  -.0100254     .01586    -0.63   0.527    -.0411107    .0210599
         88  |   .3300279   .0276529    11.93   0.000     .2758288     .384227
         89  |    .029549    .020518     1.44   0.150    -.0106658    .0697638
         90  |  -.0383272     .02122    -1.81   0.071    -.0799179    .0032636
         91  |    .249278   .0157792    15.80   0.000     .2183511     .280205
         92  |   .0013597   .0155903     0.09   0.931     -.029197    .0319163
         94  |   .0715032   .0158164     4.52   0.000     .0405034     .102503
         95  |  -.0813889   .0161154    -5.05   0.000    -.1129748   -.0498031
         96  |   .2469679   .0168901    14.62   0.000     .2138637    .2800721
         97  |  -.0600961   .0168003    -3.58   0.000    -.0930244   -.0271679
         98  |  -.0533054   .0696463    -0.77   0.444    -.1898106    .0831998
         99  |   .3171052   .0161662    19.62   0.000     .2854199    .3487906
        100  |  -.0546766   .0168033    -3.25   0.001    -.0876107   -.0217425
        101  |   -.004063   .0262662    -0.15   0.877    -.0555441    .0474181
        102  |  -.0582862     .01592    -3.66   0.000     -.089489   -.0270834
        103  |  -.0267081   .0160589    -1.66   0.096    -.0581832    .0047671
        104  |  -.0394165   .0799894    -0.49   0.622     -.196194    .1173609
        105  |   .4259792   .0164503    25.89   0.000      .393737    .4582215
        107  |   .3522551   .0201707    17.46   0.000     .3127209    .3917893
        108  |  -.0949721   .0647094    -1.47   0.142    -.2218011    .0318569
        109  |  -.0440739   .0162209    -2.72   0.007    -.0758665   -.0122812
        110  |   .0013314   .0156091     0.09   0.932    -.0292622     .031925
        111  |  -.0082374   .0186989    -0.44   0.660    -.0448868     .028412
             |
       _cons |   .0949721   .0144053     6.59   0.000      .066738    .1232061
------------------------------------------------------------------------------

. predict mentioner
(option xb assumed; fitted values)

. 
. sum mentioner if n_u==1 & opposition, det

                        Fitted values
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .0357143       .0357143
 5%     .0949721       .0416667
10%     .0949721       .0949721       Obs                  49
25%     .1520336       .0949721       Sum of Wgt.          49

50%     .2740088                      Mean           .2821138
                        Largest       Std. Dev.      .1424531
75%     .4006423        .494186
90%     .4686747        .508658       Variance       .0202929
95%      .508658       .5209513       Skewness       .0116575
99%     .5396826       .5396826       Kurtosis       1.812076

. 
. 
. label variable post_opo_ada "Post x opposition"

. label variable opposition "Opposition"

. label variable post_ada "Post"

. 
. 
. label variable mentionOther "Mention rival"

. label variable post_mention_ada "Post x Mention rival"

. 
. bysort userid: egen meanNGallT = mean(mentionOther)

. bysort userid: egen meanNGall = mean(meanNGallT)

. drop meanNGallT

. 
. gen post_ngA = post_ada*mentioner

. label variable post_ngA "Post x Negative campaigner"

. 
. 
. bysort userid: egen meanPopT = mean(log_engagement) if !post_ada
(70456 missing values generated)

. bysort userid: egen meanPop = mean(meanPopT)
(19232 missing values generated)

. drop meanPopT

. 
. gen post_pop = post_ada*meanPop
(19,232 missing values generated)

. label variable post_pop "Post x popularity"

. 
. 
. gen event_days_ada_M = event_days_ada+1000

. replace event_days_ada_M=. if event_days_ada_M<0
(21,637 real changes made, 21,637 to missing)

. bysort userid date2: gen n_tweets=_N

. bysort userid date2: gen _tweets=_n

. gen log_n_tweets=ln(n_tweets)

. 
. ***words for sentiment***
. foreach varn in si pl platforma pis org gov minister nbp tvp prezydenta polska ju tomaszsiemoniak schetynadlapo pawa kaczyski temu s
> prawapolek moe macierewicz dzi bdzie adamowicza pisorgpl newsplatforma chce bd the spotkanie salvini rzdu gociem dziki czarnecki ada
> mowicz zmian wie polsce leszczyna info dziaania dobra andruszkiewicz and adam zo wspopracy ws wpolityce wiceminister wiadomo ustawy 
> tvn tuska stycznia sowa schwertnerpl ryszard republikatv prezes pose polski polityka pastwa opozycji of nienawici ni musimy mowi mor
> awieckim morawiecki mona min konwencji kobieta kadego jestemy glapiski gdaska europa eby dorota bya brejza amadamczyk zgryglas zapra
> szam zaoenia ycie wynagrodze wyborcza wspolnota wpolscepl wop woch wiowpn wicepremiera wi warszawa trzaskowski takich szefa swoimi s
> ukiennik styczniowej spraw sowo slawomirnitras rzd rok rafalska radiomaryja pytania przyjtej projekt prezydent premiera premier pr p
> oznajcie poniej polskieradio pokaza pitek obserwatorxy myl mkierwinski ministra mierci marszaek ludzie konferencji konferencja knf j
> kmmikke jeeli jedn in gowne gdasku gazeta dulkiewicz dot dopiero dokona dnia dni deklaracji ciszy chwil byy bh beatamk barbaraanowac
> ka arkadiuszmyrcha andrzej znaczy zjednoczonaprawica zgasi zarzut zarobki zarobi zarabia zaprasza zapaci zabojstwo yczenia wywiad wy
> starczy wyrazy wszystk wskazuje wreszcie wraz wpywy wpywach wochy wobec wniosek wierzy wier wielkie wieczystego wicej wiadomoscitvp 
> wchodzi warto wanie uytkowania ust usiuje umow umiechnita ue udziau tysice tyle twarz turnieju transportu tokfm tok takiego sytuacji
>  syszelimy syna stefan stanislawski spotkaniu spoecznego solidarno sojuszu skorupka skoro skandalicznym sejm sb samym samo samemu rz
> onca rtyszkiewicz rozmowy rosyjskich rol rodziny razem rady radiowajedynka radio pytanie publ pszubartowicz psp ps projekcie program
> ie program pro prezesa premierrp pracy pplewandowski powstanie powoana potrafi porozumienie polskiej polskapolicja polityk polityczn
> ej policja polecam pogldy podlaskiepo pochwalam poar plk pkp pikne pewnie pewien pawe parlamencie pamici osi opozycj okladrewnowicz 
> odgrywa oczywicie obywatelska obecnoci nuda nowy niezaleznapl nienawi news nazywa nawzajem nauczycieli napisa najlepszym najbardziej
>  myle mylami mswia mow mniej mkidn mir midzynarodowych miasta mgolbik mediach media mdre maych mateusz lx lwoj lutego ludziach ludzi
>  lq lkwarzecha likwidacja licytacje lbalcerowicz latach kultury krzysztof kraju kraj kolegi kocu kobiet kn km kas kaczyskiego ka jut
> ro joannakluzik jerzykwiecinski jeli jbrudzinski jarosaw jaka is instytucje inf imieniu hej gramy godz gminy gk gdansk future fundac
> jawosp for fm fina fakt ego efevxqhriw dzisiejsze dziennikarzy dziaa dwoch duym duda dostp donaldtusk donalda domow dokadnie dalej d
> aje da czego ctomczyk coraz ciko cieszy chyba chwili chrzanowski chiefrobert chcia chcemy byo bm bliskimi beataszydlo beata baszczak
>  barbarzystwa asf apel antyprzemocowej andruszkiewicza amerykanami aleksandra akt aklarenbach ak aferaknf ac {
  2.         gen ggSEN_`varn' = strpos(text,"`varn' ")>0
  3. }

. 
. reg sentiment ggSEN_*
note: ggSEN_gov omitted because of collinearity
note: ggSEN_newsplatforma omitted because of collinearity
note: ggSEN_wspopracy omitted because of collinearity
note: ggSEN_wpolityce omitted because of collinearity
note: ggSEN_tvn omitted because of collinearity
note: ggSEN_konwencji omitted because of collinearity
note: ggSEN_kobieta omitted because of collinearity
note: ggSEN_dorota omitted because of collinearity
note: ggSEN_zaoenia omitted because of collinearity
note: ggSEN_wicepremiera omitted because of collinearity
note: ggSEN_trzaskowski omitted because of collinearity
note: ggSEN_styczniowej omitted because of collinearity
note: ggSEN_przyjtej omitted because of collinearity
note: ggSEN_pr omitted because of collinearity
note: ggSEN_poznajcie omitted because of collinearity
note: ggSEN_polskieradio omitted because of collinearity
note: ggSEN_marszaek omitted because of collinearity
note: ggSEN_deklaracji omitted because of collinearity
note: ggSEN_bh omitted because of collinearity
note: ggSEN_andrzej omitted because of collinearity
note: ggSEN_zjednoczonaprawica omitted because of collinearity
note: ggSEN_zgasi omitted because of collinearity
note: ggSEN_zarzut omitted because of collinearity
note: ggSEN_zarobki omitted because of collinearity
note: ggSEN_zarobi omitted because of collinearity
note: ggSEN_zarabia omitted because of collinearity
note: ggSEN_zaprasza omitted because of collinearity
note: ggSEN_zapaci omitted because of collinearity
note: ggSEN_zabojstwo omitted because of collinearity
note: ggSEN_yczenia omitted because of collinearity
note: ggSEN_wyrazy omitted because of collinearity
note: ggSEN_wszystk omitted because of collinearity
note: ggSEN_wraz omitted because of collinearity
note: ggSEN_wpywach omitted because of collinearity
note: ggSEN_wierzy omitted because of collinearity
note: ggSEN_wier omitted because of collinearity
note: ggSEN_wchodzi omitted because of collinearity
note: ggSEN_uytkowania omitted because of collinearity
note: ggSEN_usiuje omitted because of collinearity
note: ggSEN_umiechnita omitted because of collinearity
note: ggSEN_twarz omitted because of collinearity
note: ggSEN_turnieju omitted because of collinearity
note: ggSEN_transportu omitted because of collinearity
note: ggSEN_tokfm omitted because of collinearity
note: ggSEN_tok omitted because of collinearity
note: ggSEN_syszelimy omitted because of collinearity
note: ggSEN_stefan omitted because of collinearity
note: ggSEN_sojuszu omitted because of collinearity
note: ggSEN_skorupka omitted because of collinearity
note: ggSEN_skandalicznym omitted because of collinearity
note: ggSEN_sb omitted because of collinearity
note: ggSEN_samym omitted because of collinearity
note: ggSEN_rzonca omitted because of collinearity
note: ggSEN_rosyjskich omitted because of collinearity
note: ggSEN_rol omitted because of collinearity
note: ggSEN_razem omitted because of collinearity
note: ggSEN_rady omitted because of collinearity
note: ggSEN_radiowajedynka omitted because of collinearity
note: ggSEN_radio omitted because of collinearity
note: ggSEN_publ omitted because of collinearity
note: ggSEN_pszubartowicz omitted because of collinearity
note: ggSEN_psp omitted because of collinearity
note: ggSEN_projekcie omitted because of collinearity
note: ggSEN_programie omitted because of collinearity
note: ggSEN_pro omitted because of collinearity
note: ggSEN_prezesa omitted because of collinearity
note: ggSEN_powoana omitted because of collinearity
note: ggSEN_porozumienie omitted because of collinearity
note: ggSEN_polskapolicja omitted because of collinearity
note: ggSEN_polityk omitted because of collinearity
note: ggSEN_poar omitted because of collinearity
note: ggSEN_pawe omitted because of collinearity
note: ggSEN_okladrewnowicz omitted because of collinearity
note: ggSEN_nowy omitted because of collinearity
note: ggSEN_news omitted because of collinearity
note: ggSEN_nawzajem omitted because of collinearity
note: ggSEN_najbardziej omitted because of collinearity
note: ggSEN_mswia omitted because of collinearity
note: ggSEN_mkidn omitted because of collinearity
note: ggSEN_mir omitted because of collinearity
note: ggSEN_mdre omitted because of collinearity
note: ggSEN_maych omitted because of collinearity
note: ggSEN_mateusz omitted because of collinearity
note: ggSEN_lx omitted because of collinearity
note: ggSEN_lwoj omitted because of collinearity
note: ggSEN_ludziach omitted because of collinearity
note: ggSEN_lq omitted because of collinearity
note: ggSEN_likwidacja omitted because of collinearity
note: ggSEN_latach omitted because of collinearity
note: ggSEN_kraj omitted because of collinearity
note: ggSEN_kobiet omitted because of collinearity
note: ggSEN_kas omitted because of collinearity
note: ggSEN_joannakluzik omitted because of collinearity
note: ggSEN_jbrudzinski omitted because of collinearity
note: ggSEN_jaka omitted because of collinearity
note: ggSEN_inf omitted because of collinearity
note: ggSEN_gramy omitted because of collinearity
note: ggSEN_godz omitted because of collinearity
note: ggSEN_gk omitted because of collinearity
note: ggSEN_dziennikarzy omitted because of collinearity
note: ggSEN_duda omitted because of collinearity
note: ggSEN_donaldtusk omitted because of collinearity
note: ggSEN_domow omitted because of collinearity
note: ggSEN_dalej omitted because of collinearity
note: ggSEN_ctomczyk omitted because of collinearity
note: ggSEN_cieszy omitted because of collinearity
note: ggSEN_chrzanowski omitted because of collinearity
note: ggSEN_chiefrobert omitted because of collinearity
note: ggSEN_chcemy omitted because of collinearity
note: ggSEN_bm omitted because of collinearity
note: ggSEN_bliskimi omitted because of collinearity
note: ggSEN_beata omitted because of collinearity
note: ggSEN_barbarzystwa omitted because of collinearity
note: ggSEN_asf omitted because of collinearity
note: ggSEN_apel omitted because of collinearity
note: ggSEN_antyprzemocowej omitted because of collinearity
note: ggSEN_amerykanami omitted because of collinearity
note: ggSEN_aferaknf omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =       300
-------------+----------------------------------   F(272, 27)      =      2.44
       Model |  120.851557       272  .444307196   Prob > F        =    0.0036
    Residual |  4.91844275        27  .182164546   R-squared       =    0.9609
-------------+----------------------------------   Adj R-squared   =    0.5669
       Total |      125.77       299  .420635452   Root MSE        =    .42681

------------------------------------------------------------------------------------------
               sentiment |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                ggSEN_si |   .2197405   .3476258     0.63   0.533    -.4935288    .9330097
                ggSEN_pl |  -.3670396   .2679123    -1.37   0.182    -.9167502    .1826711
         ggSEN_platforma |  -1.745317   .6207926    -2.81   0.009    -3.019079   -.4715562
               ggSEN_pis |  -2.093748   2.986768    -0.70   0.489     -8.22209    4.034594
               ggSEN_org |   .0439343   1.463326     0.03   0.976    -2.958562    3.046431
               ggSEN_gov |          0  (omitted)
          ggSEN_minister |   -.017346   .8151568    -0.02   0.983     -1.68991    1.655218
               ggSEN_nbp |   -1.01478   .4482828    -2.26   0.032     -1.93458   -.0949795
               ggSEN_tvp |  -.8674807   .5658209    -1.53   0.137    -2.028449     .293488
        ggSEN_prezydenta |   5.499898   5.408955     1.02   0.318     -5.59836    16.59816
            ggSEN_polska |   .2028067   2.700962     0.08   0.941     -5.33911    5.744724
                ggSEN_ju |   .6769034   1.077899     0.63   0.535    -1.534762    2.888569
   ggSEN_tomaszsiemoniak |  -.6040954   2.002093    -0.30   0.765    -4.712051     3.50386
     ggSEN_schetynadlapo |  -.1229176   .6347679    -0.19   0.848    -1.425354    1.179519
              ggSEN_pawa |   -8.96698   3.495772    -2.57   0.016    -16.13971   -1.794248
          ggSEN_kaczyski |  -1.386265   1.367533    -1.01   0.320     -4.19221    1.419681
              ggSEN_temu |  -.1988621   .4588606    -0.43   0.668    -1.140366     .742642
       ggSEN_sprawapolek |   1.241868   1.555934     0.80   0.432    -1.950646    4.434381
               ggSEN_moe |  -.1376974   .4539772    -0.30   0.764    -1.069182    .7937868
       ggSEN_macierewicz |   1.103932   3.819392     0.29   0.775    -6.732812    8.940677
               ggSEN_dzi |   .6836412   .4659435     1.47   0.154    -.2723959    1.639678
             ggSEN_bdzie |  -.0705619   .6265882    -0.11   0.911    -1.356215    1.215091
        ggSEN_adamowicza |   3.523919   4.747772     0.74   0.464    -6.217704    13.26554
          ggSEN_pisorgpl |  -2.189732   1.088299    -2.01   0.054    -4.422736    .0432726
     ggSEN_newsplatforma |          0  (omitted)
              ggSEN_chce |   .3017441   1.559808     0.19   0.848    -2.898718    3.502206
                ggSEN_bd |  -4.576825   2.137344    -2.14   0.041    -8.962292   -.1913581
               ggSEN_the |  -1.616027   2.691409    -0.60   0.553    -7.138342    3.906289
         ggSEN_spotkanie |   1.405604   3.147401     0.45   0.659     -5.05233    7.863537
           ggSEN_salvini |     .88317   2.068936     0.43   0.673    -3.361937    5.128277
              ggSEN_rzdu |  -8.114743   5.109293    -1.59   0.124    -18.59815     2.36866
            ggSEN_gociem |   .4917832   1.847606     0.27   0.792     -3.29919    4.282757
             ggSEN_dziki |  -.1376974   .2909506    -0.47   0.640    -.7346786    .4592839
         ggSEN_czarnecki |  -2.357373    2.76511    -0.85   0.401    -8.030912    3.316165
         ggSEN_adamowicz |   .9852202   .4482828     2.20   0.037       .06542     1.90502
             ggSEN_zmian |   5.617934     2.8548     1.97   0.059    -.2396306     11.4755
               ggSEN_wie |  -1.300582   .6579834    -1.98   0.058    -2.650652    .0494886
            ggSEN_polsce |  -1.070562   .6265882    -1.71   0.099    -2.356215    .2150908
         ggSEN_leszczyna |   .7135588   .9312971     0.77   0.450    -1.197305    2.624423
              ggSEN_info |     .93868   1.498233     0.63   0.536     -2.13544      4.0128
          ggSEN_dziaania |   -.053633   1.013422    -0.05   0.958    -2.133003    2.025737
             ggSEN_dobra |   .8783751   2.916035     0.30   0.766    -5.104835    6.861585
    ggSEN_andruszkiewicz |   .0025663    .869127     0.00   0.998    -1.780735    1.785868
               ggSEN_and |   .9852202   .4482828     2.20   0.037       .06542     1.90502
              ggSEN_adam |   3.74e-13   .6035968     0.00   1.000    -1.238478    1.238478
                ggSEN_zo |   3.003043   3.122724     0.96   0.345    -3.404259    9.410344
         ggSEN_wspopracy |          0  (omitted)
                ggSEN_ws |  -.4542524   2.488195    -0.18   0.857    -5.559606    4.651101
         ggSEN_wpolityce |          0  (omitted)
      ggSEN_wiceminister |  -.1431231   1.274327    -0.11   0.911    -2.757827    2.471581
           ggSEN_wiadomo |  -5.396249   3.539637    -1.52   0.139    -12.65898    1.866485
            ggSEN_ustawy |  -5.911648   3.846503    -1.54   0.136    -13.80402    1.980725
               ggSEN_tvn |          0  (omitted)
             ggSEN_tuska |  -4.337347   2.150364    -2.02   0.054    -8.749528    .0748349
          ggSEN_stycznia |  -2.276209   1.730908    -1.32   0.200    -5.827738     1.27532
              ggSEN_sowa |  -1.442165   2.624595    -0.55   0.587     -6.82739    3.943059
      ggSEN_schwertnerpl |   .3522598   .5007851     0.70   0.488    -.6752664    1.379786
           ggSEN_ryszard |   .9852202   .4482828     2.20   0.037       .06542     1.90502
       ggSEN_republikatv |  -.1376974   .4539772    -0.30   0.764    -1.069182    .7937868
            ggSEN_prezes |  -8.839569   4.391214    -2.01   0.054     -17.8496    .1704572
              ggSEN_pose |   2.223878   2.925413     0.76   0.454    -3.778573    8.226329
            ggSEN_polski |   .8592428   1.695417     0.51   0.616    -2.619466    4.337951
          ggSEN_polityka |  -.0705619   .6265882    -0.11   0.911    -1.356215    1.215091
            ggSEN_pastwa |  -2.878609   5.962273    -0.48   0.633    -15.11218    9.354965
          ggSEN_opozycji |   1.096978   1.369813     0.80   0.430    -1.713645    3.907601
                ggSEN_of |  -1.137697   .4539772    -2.51   0.019    -2.069182   -.2062132
         ggSEN_nienawici |  -2.321229   2.019665    -1.15   0.261    -6.465238     1.82278
                ggSEN_ni |  -.5906412   .7558285    -0.78   0.441    -2.141473    .9601909
            ggSEN_musimy |  -1.427225   1.647732    -0.87   0.394    -4.808091    1.953642
              ggSEN_mowi |  -1.221675   2.557629    -0.48   0.637    -6.469496    4.026145
       ggSEN_morawieckim |   -.337838    1.34675    -0.25   0.804    -3.101141    2.425465
        ggSEN_morawiecki |  -1.797777   2.877862    -0.62   0.537    -7.702663    4.107109
              ggSEN_mona |  -.0147798   .4482828    -0.03   0.974      -.93458    .9050205
               ggSEN_min |  -1.263126   2.809244    -0.45   0.657    -7.027218    4.500966
         ggSEN_konwencji |          0  (omitted)
           ggSEN_kobieta |          0  (omitted)
            ggSEN_kadego |   1.435154   1.429825     1.00   0.324    -1.498603    4.368912
           ggSEN_jestemy |   1.540699   2.609226     0.59   0.560    -3.812992    6.894389
          ggSEN_glapiski |   3.466803   1.745232     1.99   0.057    -.1141179    7.047724
            ggSEN_gdaska |   -4.42366   3.416887    -1.29   0.206    -11.43453    2.587214
            ggSEN_europa |   1.463819   2.377616     0.62   0.543    -3.414645    6.342283
               ggSEN_eby |  -.2107398     2.8005    -0.08   0.941    -5.956892    5.535412
            ggSEN_dorota |          0  (omitted)
               ggSEN_bya |  -.6082976   .7327547    -0.83   0.414    -2.111786    .8951909
            ggSEN_brejza |  -2.169456   1.294738    -1.68   0.105    -4.826039    .4871274
        ggSEN_amadamczyk |   2.411569   .9056145     2.66   0.013     .5534019    4.269737
          ggSEN_zgryglas |    3.23787   4.776534     0.68   0.504    -6.562768    13.03851
         ggSEN_zapraszam |   -.229205   1.869878    -0.12   0.903    -4.065878    3.607468
           ggSEN_zaoenia |          0  (omitted)
              ggSEN_ycie |   .8456818   .8201246     1.03   0.312     -.837075    2.528439
        ggSEN_wynagrodze |   9.376883   4.974893     1.88   0.070    -.8307551    19.58452
          ggSEN_wyborcza |   1.286922   2.648082     0.49   0.631    -4.146493    6.720338
         ggSEN_wspolnota |  -1.058714   1.394935    -0.76   0.454    -3.920884    1.803456
         ggSEN_wpolscepl |   3.997859   3.401289     1.18   0.250    -2.981009    10.97673
               ggSEN_wop |  -.0147798   .4482828    -0.03   0.974      -.93458    .9050205
              ggSEN_woch |  -7.540186     4.9524    -1.52   0.140    -17.70167      2.6213
            ggSEN_wiowpn |   1.990398   1.808555     1.10   0.281    -1.720451    5.701247
      ggSEN_wicepremiera |          0  (omitted)
                ggSEN_wi |  -2.474615   1.579912    -1.57   0.129    -5.716326    .7670964
          ggSEN_warszawa |  -.0762386   .3206147    -0.24   0.814    -.7340855    .5816084
       ggSEN_trzaskowski |          0  (omitted)
            ggSEN_takich |   5.829962   4.463846     1.31   0.203    -3.329094    14.98902
             ggSEN_szefa |   1.470684   3.272237     0.45   0.657    -5.243393    8.184761
            ggSEN_swoimi |   2.668222   3.289601     0.81   0.424    -4.081481    9.417926
         ggSEN_sukiennik |    1.01478   .7518554     1.35   0.188    -.5279001     2.55746
       ggSEN_styczniowej |          0  (omitted)
             ggSEN_spraw |   4.216245   2.670141     1.58   0.126    -1.262432    9.694922
              ggSEN_sowo |   1.187053    1.15028     1.03   0.311    -1.173126    3.547233
    ggSEN_slawomirnitras |   1.659814   3.579869     0.46   0.647    -5.685471    9.005099
               ggSEN_rzd |  -2.111741   2.540629    -0.83   0.413    -7.324681      3.1012
               ggSEN_rok |  -1.201833   1.148855    -1.05   0.305    -3.559088    1.155422
          ggSEN_rafalska |   -.295685   1.068123    -0.28   0.784    -2.487292    1.895922
       ggSEN_radiomaryja |   .9187541   3.188492     0.29   0.775    -5.623492       7.461
           ggSEN_pytania |  -.6223998   .5887002    -1.06   0.300    -1.830313    .5855133
          ggSEN_przyjtej |          0  (omitted)
           ggSEN_projekt |  -2.793113   1.989596    -1.40   0.172    -6.875427    1.289201
         ggSEN_prezydent |   .0862622   .5825798     0.15   0.883    -1.109093    1.281617
          ggSEN_premiera |   5.357587   4.651867     1.15   0.260    -4.187256    14.90243
           ggSEN_premier |  -.0523935   2.507151    -0.02   0.983    -5.196643    5.091856
                ggSEN_pr |          0  (omitted)
         ggSEN_poznajcie |          0  (omitted)
            ggSEN_poniej |   3.962998    2.91343     1.36   0.185    -2.014867    9.940862
      ggSEN_polskieradio |          0  (omitted)
            ggSEN_pokaza |  -1.718383   3.051683    -0.56   0.578    -7.979919    4.543153
             ggSEN_pitek |   2.173101   2.962445     0.73   0.470    -3.905333    8.251535
      ggSEN_obserwatorxy |  -.1376974   .4539772    -0.30   0.764    -1.069182    .7937868
               ggSEN_myl |  -1.918877   .9132264    -2.10   0.045    -3.792663   -.0450917
       ggSEN_mkierwinski |   2.457337    4.38399     0.56   0.580    -6.537867    11.45254
          ggSEN_ministra |  -1.804304   2.844951    -0.63   0.531    -7.641661    4.033053
            ggSEN_mierci |    4.31985   3.353015     1.29   0.209    -2.559968    11.19967
          ggSEN_marszaek |          0  (omitted)
            ggSEN_ludzie |   2.407479    1.24852     1.93   0.064    -.1542713     4.96923
       ggSEN_konferencji |   -.839027   2.764992    -0.30   0.764    -6.512321    4.834267
       ggSEN_konferencja |   -1.41322   .8211712    -1.72   0.097    -3.098124    .2716841
               ggSEN_knf |   4.675152   4.524708     1.03   0.311    -4.608781    13.95909
          ggSEN_jkmmikke |  -.0147798   .3314742    -0.04   0.965    -.6949087    .6653492
             ggSEN_jeeli |   4.424188   2.674291     1.65   0.110    -1.063005     9.91138
              ggSEN_jedn |    1.38704   2.884815     0.48   0.635    -4.532112    7.306192
                ggSEN_in |   1.483863   2.921209     0.51   0.616    -4.509962    7.477687
             ggSEN_gowne |  -3.145907    1.78047    -1.77   0.089    -6.799129    .5073149
            ggSEN_gdasku |   4.320608   5.398107     0.80   0.430    -6.755392    15.39661
            ggSEN_gazeta |  -1.749187   2.738723    -0.64   0.528    -7.368581    3.870208
        ggSEN_dulkiewicz |  -3.943757   5.775477    -0.68   0.501    -15.79406    7.906544
               ggSEN_dot |  -2.421893   2.643758    -0.92   0.368    -7.846437    3.002652
           ggSEN_dopiero |  -.0465384    .686378    -0.07   0.946     -1.45487    1.361793
            ggSEN_dokona |   .5204373   .6111061     0.85   0.402     -.733449    1.774323
              ggSEN_dnia |  -.2491114   1.679328    -0.15   0.883    -3.694807    3.196584
               ggSEN_dni |   .3776004   3.023116     0.12   0.902    -5.825321    6.580522
        ggSEN_deklaracji |          0  (omitted)
             ggSEN_ciszy |   3.179279   2.982886     1.07   0.296    -2.941099    9.299656
             ggSEN_chwil |   .0932893   3.255228     0.03   0.977    -6.585886    6.772465
               ggSEN_byy |   2.083157   1.853251     1.12   0.271      -1.7194    5.885715
                ggSEN_bh |          0  (omitted)
           ggSEN_beatamk |  -2.608695   1.608594    -1.62   0.116    -5.909258    .6918683
   ggSEN_barbaraanowacka |  -3.075345   1.484238    -2.07   0.048    -6.120749   -.0299412
   ggSEN_arkadiuszmyrcha |  -4.793385    2.64476    -1.81   0.081    -10.21998    .6332138
           ggSEN_andrzej |          0  (omitted)
            ggSEN_znaczy |   7.621433   3.284419     2.32   0.028     .8823617     14.3605
ggSEN_zjednoczonaprawica |          0  (omitted)
             ggSEN_zgasi |          0  (omitted)
            ggSEN_zarzut |          0  (omitted)
           ggSEN_zarobki |          0  (omitted)
            ggSEN_zarobi |          0  (omitted)
           ggSEN_zarabia |          0  (omitted)
          ggSEN_zaprasza |          0  (omitted)
            ggSEN_zapaci |          0  (omitted)
         ggSEN_zabojstwo |          0  (omitted)
           ggSEN_yczenia |          0  (omitted)
            ggSEN_wywiad |   -2.05931   .8300899    -2.48   0.020    -3.762513   -.3561059
         ggSEN_wystarczy |  -.1934795   .5783468    -0.33   0.741    -1.380149      .99319
            ggSEN_wyrazy |          0  (omitted)
           ggSEN_wszystk |          0  (omitted)
          ggSEN_wskazuje |  -3.714319   3.632441    -1.02   0.316    -11.16747    3.738835
          ggSEN_wreszcie |   4.447312   6.075695     0.73   0.470    -8.018984    16.91361
              ggSEN_wraz |          0  (omitted)
             ggSEN_wpywy |  -1.942189   3.050669    -0.64   0.530    -8.201646    4.317267
           ggSEN_wpywach |          0  (omitted)
             ggSEN_wochy |   5.108124   2.729134     1.87   0.072    -.4915969    10.70785
             ggSEN_wobec |  -6.107365    4.08049    -1.50   0.146    -14.47984    2.265108
           ggSEN_wniosek |  -6.068598   4.135363    -1.47   0.154    -14.55366    2.416466
            ggSEN_wierzy |          0  (omitted)
              ggSEN_wier |          0  (omitted)
           ggSEN_wielkie |  -2.543379   4.030236    -0.63   0.533    -10.81274    5.725981
       ggSEN_wieczystego |  -.9014499   .8884839    -1.01   0.319    -2.724468    .9215684
             ggSEN_wicej |  -.4525597   1.772973    -0.26   0.800    -4.090399     3.18528
     ggSEN_wiadomoscitvp |   .3304244   1.822832     0.18   0.858    -3.409717    4.070566
           ggSEN_wchodzi |          0  (omitted)
             ggSEN_warto |  -4.809086   2.741316    -1.75   0.091     -10.4338    .8156307
             ggSEN_wanie |   2.004783   1.317302     1.52   0.140     -.698097    4.707663
        ggSEN_uytkowania |          0  (omitted)
               ggSEN_ust |   5.988756   5.406085     1.11   0.278    -5.103614    17.08113
            ggSEN_usiuje |          0  (omitted)
              ggSEN_umow |    4.50843   2.144393     2.10   0.045     .1084998    8.908361
        ggSEN_umiechnita |          0  (omitted)
                ggSEN_ue |  -5.184492   4.129028    -1.26   0.220    -13.65656    3.287573
            ggSEN_udziau |   1.791946   1.030425     1.74   0.093    -.3223117    3.906205
            ggSEN_tysice |  -2.492008   2.279827    -1.09   0.284    -7.169826    2.185811
              ggSEN_tyle |   4.652356   1.468087     3.17   0.004      1.64009    7.664621
             ggSEN_twarz |          0  (omitted)
          ggSEN_turnieju |          0  (omitted)
        ggSEN_transportu |          0  (omitted)
             ggSEN_tokfm |          0  (omitted)
               ggSEN_tok |          0  (omitted)
           ggSEN_takiego |   .0452599   .6798319     0.07   0.947     -1.34964     1.44016
          ggSEN_sytuacji |  -1.514548   3.354874    -0.45   0.655    -8.398182    5.369086
         ggSEN_syszelimy |          0  (omitted)
              ggSEN_syna |  -3.180472   1.353598    -2.35   0.026    -5.957826   -.4031178
            ggSEN_stefan |          0  (omitted)
      ggSEN_stanislawski |  -.0147798   .3314742    -0.04   0.965    -.6949087    .6653492
         ggSEN_spotkaniu |  -.5433776   1.061895    -0.51   0.613    -2.722205     1.63545
        ggSEN_spoecznego |  -1.745317   .8658594    -2.02   0.054    -3.521914    .0312794
         ggSEN_solidarno |   1.524209   2.689283     0.57   0.576    -3.993745    7.042162
           ggSEN_sojuszu |          0  (omitted)
          ggSEN_skorupka |          0  (omitted)
             ggSEN_skoro |   3.344661    2.50594     1.33   0.193    -1.797104    8.486426
     ggSEN_skandalicznym |          0  (omitted)
              ggSEN_sejm |   1.127743     1.2922     0.87   0.391    -1.523633     3.77912
                ggSEN_sb |          0  (omitted)
             ggSEN_samym |          0  (omitted)
              ggSEN_samo |   -2.92088   4.567414    -0.64   0.528    -12.29244     6.45068
            ggSEN_samemu |   4.374508   4.875198     0.90   0.377    -5.628571    14.37759
            ggSEN_rzonca |          0  (omitted)
      ggSEN_rtyszkiewicz |   .2222592   3.952097     0.06   0.956    -7.886773    8.331292
           ggSEN_rozmowy |  -.3779233   2.469989    -0.15   0.880    -5.445923    4.690076
        ggSEN_rosyjskich |          0  (omitted)
               ggSEN_rol |          0  (omitted)
           ggSEN_rodziny |  -4.202995    2.04824    -2.05   0.050    -8.405636   -.0003536
             ggSEN_razem |          0  (omitted)
              ggSEN_rady |          0  (omitted)
    ggSEN_radiowajedynka |          0  (omitted)
             ggSEN_radio |          0  (omitted)
           ggSEN_pytanie |  -7.070132   4.023508    -1.76   0.090    -15.32569    1.185424
              ggSEN_publ |          0  (omitted)
     ggSEN_pszubartowicz |          0  (omitted)
               ggSEN_psp |          0  (omitted)
                ggSEN_ps |  -.1229176    .196472    -0.63   0.537    -.5260448    .2802096
         ggSEN_projekcie |          0  (omitted)
         ggSEN_programie |          0  (omitted)
           ggSEN_program |   2.336917   1.615019     1.45   0.159    -.9768282    5.650663
               ggSEN_pro |          0  (omitted)
           ggSEN_prezesa |          0  (omitted)
         ggSEN_premierrp |   .3544589    1.75091     0.20   0.841    -3.238111    3.947029
             ggSEN_pracy |   5.893276   2.851007     2.07   0.048     .0434927    11.74306
     ggSEN_pplewandowski |  -.0147798   .4482828    -0.03   0.974      -.93458    .9050205
         ggSEN_powstanie |  -1.806959   2.730136    -0.66   0.514    -7.408735    3.794816
           ggSEN_powoana |          0  (omitted)
           ggSEN_potrafi |  -5.075778   2.865154    -1.77   0.088    -10.95459    .8030327
      ggSEN_porozumienie |          0  (omitted)
          ggSEN_polskiej |  -4.400989   5.128371    -0.86   0.398    -14.92354     6.12156
     ggSEN_polskapolicja |          0  (omitted)
           ggSEN_polityk |          0  (omitted)
       ggSEN_politycznej |  -1.386309    2.27913    -0.61   0.548    -6.062698     3.29008
           ggSEN_policja |   .1735601   .6482526     0.27   0.791    -1.156544    1.503665
           ggSEN_polecam |   .6425622   .5644269     1.14   0.265    -.5155462    1.800671
            ggSEN_pogldy |  -6.681825   4.003477    -1.67   0.107    -14.89628     1.53263
       ggSEN_podlaskiepo |  -4.969661   4.158873    -1.19   0.242    -13.50296    3.563642
         ggSEN_pochwalam |  -6.147629    2.84295    -2.16   0.040    -11.98088   -.3143779
              ggSEN_poar |          0  (omitted)
               ggSEN_plk |   4.044719   2.229046     1.81   0.081    -.5289069    8.618344
               ggSEN_pkp |  -1.921612   1.069957    -1.80   0.084    -4.116983    .2737587
             ggSEN_pikne |   2.154598   2.628563     0.82   0.420    -3.238768    7.547964
            ggSEN_pewnie |  -.6211213   1.102129    -0.56   0.578    -2.882503     1.64026
            ggSEN_pewien |    8.64609   4.610252     1.88   0.072    -.8133653    18.10554
              ggSEN_pawe |          0  (omitted)
       ggSEN_parlamencie |  -2.010716   1.453347    -1.38   0.178    -4.992738    .9713061
            ggSEN_pamici |   7.219274   3.287168     2.20   0.037     .4745623    13.96399
               ggSEN_osi |   .9785207   1.053218     0.93   0.361    -1.182503    3.139545
           ggSEN_opozycj |    7.13374   4.040806     1.77   0.089     -1.15731    15.42479
    ggSEN_okladrewnowicz |          0  (omitted)
           ggSEN_odgrywa |  -.6223998   .5887002    -1.06   0.300    -1.830313    .5855133
         ggSEN_oczywicie |  -1.223482   3.145557    -0.39   0.700    -7.677633    5.230669
       ggSEN_obywatelska |  -2.869538   3.110388    -0.92   0.364    -9.251527    3.512452
          ggSEN_obecnoci |  -.1376974   .4539772    -0.30   0.764    -1.069182    .7937868
              ggSEN_nuda |  -.4834483    .936875    -0.52   0.610    -2.405757     1.43886
              ggSEN_nowy |          0  (omitted)
      ggSEN_niezaleznapl |   6.261557   3.019662     2.07   0.048     .0657213    12.45739
           ggSEN_nienawi |    2.68151   2.117847     1.27   0.216    -1.663953    7.026973
              ggSEN_news |          0  (omitted)
            ggSEN_nazywa |  -6.928177   6.529109    -1.06   0.298     -20.3248    6.468449
          ggSEN_nawzajem |          0  (omitted)
       ggSEN_nauczycieli |  -.0587141   1.404643    -0.04   0.967    -2.940803    2.823375
            ggSEN_napisa |  -.7959982   .4289615    -1.86   0.074    -1.676155    .0841581
        ggSEN_najlepszym |  -.0147798   .4482828    -0.03   0.974      -.93458    .9050205
       ggSEN_najbardziej |          0  (omitted)
              ggSEN_myle |   3.113828   1.599478     1.95   0.062    -.1680304    6.395686
            ggSEN_mylami |  -1.286016   2.507941    -0.51   0.612    -6.431885    3.859853
             ggSEN_mswia |          0  (omitted)
               ggSEN_mow |  -4.037782   2.107633    -1.92   0.066    -8.362289     .286724
             ggSEN_mniej |    5.14184   2.708894     1.90   0.068     -.416352    10.70003
             ggSEN_mkidn |          0  (omitted)
               ggSEN_mir |          0  (omitted)
   ggSEN_midzynarodowych |   -1.62156   2.947758    -0.55   0.587    -7.669859    4.426739
            ggSEN_miasta |  -.3259988   .8703914    -0.37   0.711    -2.111894    1.459897
           ggSEN_mgolbik |   4.065047   2.299782     1.77   0.088    -.6537159    8.783809
           ggSEN_mediach |   2.558593   1.969871     1.30   0.205    -1.483249    6.600435
             ggSEN_media |  -.2197405   .6965435    -0.32   0.755     -1.64893    1.209449
              ggSEN_mdre |          0  (omitted)
             ggSEN_maych |          0  (omitted)
           ggSEN_mateusz |          0  (omitted)
                ggSEN_lx |          0  (omitted)
              ggSEN_lwoj |          0  (omitted)
            ggSEN_lutego |  -.7453174   .6207926    -1.20   0.240    -2.019079    .5284438
          ggSEN_ludziach |          0  (omitted)
             ggSEN_ludzi |   .5090631    1.63396     0.31   0.758    -2.843545    3.861671
                ggSEN_lq |          0  (omitted)
        ggSEN_lkwarzecha |  -2.278212   1.411195    -1.61   0.118    -5.173745    .6173211
        ggSEN_likwidacja |          0  (omitted)
         ggSEN_licytacje |    -.60762   .7375725    -0.82   0.417    -2.120994    .9057538
      ggSEN_lbalcerowicz |   5.253421   6.792778     0.77   0.446    -8.684207    19.19105
            ggSEN_latach |          0  (omitted)
           ggSEN_kultury |   .0357255   1.569962     0.02   0.982    -3.185571    3.257022
         ggSEN_krzysztof |          1   .6035968     1.66   0.109    -.2384783    2.238478
             ggSEN_kraju |   3.542484   1.673612     2.12   0.044     .1085153    6.976453
              ggSEN_kraj |          0  (omitted)
            ggSEN_kolegi |   1.264007   2.844793     0.44   0.660    -4.573025     7.10104
              ggSEN_kocu |   -.263057   1.571714    -0.17   0.868    -3.487947    2.961833
            ggSEN_kobiet |          0  (omitted)
                ggSEN_kn |   .0188205   2.721324     0.01   0.995    -5.564876    5.602517
                ggSEN_km |   -1.87135    2.92342    -0.64   0.527    -7.869712    4.127012
               ggSEN_kas |          0  (omitted)
       ggSEN_kaczyskiego |  -3.013224   3.448821    -0.87   0.390    -10.08962    4.063173
                ggSEN_ka |   .0557821   .4090896     0.14   0.893    -.7836004    .8951646
             ggSEN_jutro |   .0915076   1.831569     0.05   0.961    -3.666561    3.849577
      ggSEN_joannakluzik |          0  (omitted)
   ggSEN_jerzykwiecinski |   2.308676   1.864705     1.24   0.226    -1.517382    6.134735
              ggSEN_jeli |   9.258768   3.893958     2.38   0.025     1.269025    17.24851
       ggSEN_jbrudzinski |          0  (omitted)
           ggSEN_jarosaw |  -2.013948   1.652915    -1.22   0.234    -5.405449    1.377554
              ggSEN_jaka |          0  (omitted)
                ggSEN_is |   1.645586   2.787228     0.59   0.560    -4.073333    7.364505
        ggSEN_instytucje |   .9852202   .7518554     1.31   0.201    -.5574596      2.5279
               ggSEN_inf |          0  (omitted)
           ggSEN_imieniu |   -2.35652    3.57504    -0.66   0.515    -9.691896    4.978856
               ggSEN_hej |   2.659165   2.360103     1.13   0.270    -2.183367    7.501697
             ggSEN_gramy |          0  (omitted)
              ggSEN_godz |          0  (omitted)
             ggSEN_gminy |  -6.030973   2.887759    -2.09   0.046    -11.95617   -.1057808
                ggSEN_gk |          0  (omitted)
            ggSEN_gdansk |  -1.712432   .8301639    -2.06   0.049    -3.415788   -.0090769
            ggSEN_future |   .4289997   1.966471     0.22   0.829    -3.605866    4.463865
      ggSEN_fundacjawosp |   3.252611   2.227418     1.46   0.156    -1.317673    7.822895
               ggSEN_for |   .0092464   1.703459     0.01   0.996    -3.485963    3.504456
                ggSEN_fm |   .7693409   1.248846     0.62   0.543    -1.793079    3.331761
              ggSEN_fina |  -.6769034   1.235393    -0.55   0.588     -3.21172    1.857913
              ggSEN_fakt |  -4.581645   5.719013    -0.80   0.430    -16.31609      7.1528
               ggSEN_ego |     .60762   .4238917     1.43   0.163    -.2621338    1.477374
        ggSEN_efevxqhriw |  -2.896197    4.93199    -0.59   0.562     -13.0158     7.22341
        ggSEN_dzisiejsze |   .2068562    4.18271     0.05   0.961    -8.375356    8.789068
      ggSEN_dziennikarzy |          0  (omitted)
             ggSEN_dziaa |  -1.424535   1.335022    -1.07   0.295    -4.163775    1.314705
             ggSEN_dwoch |    8.71886   3.147963     2.77   0.010     2.259773    15.17795
              ggSEN_duym |  -.1934795   .5783468    -0.33   0.741    -1.380149      .99319
              ggSEN_duda |          0  (omitted)
             ggSEN_dostp |  -2.189769   2.854686    -0.77   0.450      -8.0471    3.667563
        ggSEN_donaldtusk |          0  (omitted)
           ggSEN_donalda |   6.313038   2.553423     2.47   0.020     1.073846    11.55223
             ggSEN_domow |          0  (omitted)
          ggSEN_dokadnie |   .0867158   2.591482     0.03   0.974    -5.230567    5.403999
             ggSEN_dalej |          0  (omitted)
              ggSEN_daje |   2.593915   1.603322     1.62   0.117      -.69583     5.88366
                ggSEN_da |  -.0636079   .7254919    -0.09   0.931    -1.552194    1.424979
             ggSEN_czego |    -1.6224   .5887002    -2.76   0.010    -2.830313   -.4144867
          ggSEN_ctomczyk |          0  (omitted)
             ggSEN_coraz |  -1.082712    2.95553    -0.37   0.717     -7.14696    4.981535
              ggSEN_ciko |   .8623026   .4539772     1.90   0.068    -.0691816    1.793787
            ggSEN_cieszy |          0  (omitted)
             ggSEN_chyba |  -1.768519   1.802016    -0.98   0.335    -5.465949    1.928912
            ggSEN_chwili |   .4792431   .8026205     0.60   0.555    -1.167598    2.126084
       ggSEN_chrzanowski |          0  (omitted)
       ggSEN_chiefrobert |          0  (omitted)
             ggSEN_chcia |    .073786   3.774701     0.02   0.985    -7.671261    7.818833
            ggSEN_chcemy |          0  (omitted)
               ggSEN_byo |  -5.967659    4.48249    -1.33   0.194    -15.16497    3.229651
                ggSEN_bm |          0  (omitted)
          ggSEN_bliskimi |          0  (omitted)
       ggSEN_beataszydlo |    .855603   2.966887     0.29   0.775    -5.231946    6.943152
             ggSEN_beata |          0  (omitted)
          ggSEN_baszczak |  -4.963181   3.345564    -1.48   0.150    -11.82771    1.901349
      ggSEN_barbarzystwa |          0  (omitted)
               ggSEN_asf |          0  (omitted)
              ggSEN_apel |          0  (omitted)
   ggSEN_antyprzemocowej |          0  (omitted)
   ggSEN_andruszkiewicza |  -1.099002   3.625397    -0.30   0.764    -8.537701    6.339698
       ggSEN_amerykanami |          0  (omitted)
        ggSEN_aleksandra |   2.816056   6.089601     0.46   0.647    -9.678772    15.31088
               ggSEN_akt |   2.912601   2.589516     1.12   0.271    -2.400648     8.22585
       ggSEN_aklarenbach |   -1.62156   2.954659    -0.55   0.588    -7.684019    4.440898
                ggSEN_ak |  -.2239596   .3749895    -0.60   0.555    -.9933744    .5454552
          ggSEN_aferaknf |          0  (omitted)
                ggSEN_ac |  -3.732839   2.211352    -1.69   0.103    -8.270157    .8044801
                   _cons |   .1376974   .1546955     0.89   0.381    -.1797117    .4551064
------------------------------------------------------------------------------------------

. predict sentimentPR
(option xb assumed; fitted values)

. 
. gen positive = 1 if sentimentPR>0
(109,550 missing values generated)

. replace positive = 0 if sentimentPR<=0
(109,550 real changes made)

. 
. 
. 
. **** Figure A9 ***
. 
. 
. cibar sentiment if timeWindow3_ada, over1(mentionOther) over2(post_ada)

. cibar sentimentPR if timeWindow3_ada, over1(mentionOther) over2(post_ada)

. 
. 
. 
. **** Figure A10 ***
. 
. 
. reghdfe log_engagement ib1014.event_days_ada_M##positive if timeWindow3_ada & !gg_rt, abs(userid)
(dropped 2 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 1 HDFE group                            F(  59,   3832) =       5.49
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5673
                                                  Adj R-squared   =     0.5511
                                                  Within R-sq.    =     0.0779
                                                  Root MSE        =     1.4747

-------------------------------------------------------------------------------------------
           log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
         event_days_ada_M |
                     985  |  -.8045159   .3027231    -2.66   0.008     -1.39803    -.211002
                     986  |  -.5074423   .3142965    -1.61   0.106    -1.123647    .1087621
                     987  |   .2850101   .2981108     0.96   0.339     -.299461    .8694811
                     988  |   .5728829   .4064503     1.41   0.159    -.2239968    1.369763
                     989  |   .5230712   .3081709     1.70   0.090    -.0811235    1.127266
                     990  |    .393895   .3022408     1.30   0.193    -.1986733    .9864633
                     991  |   .2363167   .2924084     0.81   0.419    -.3369744    .8096078
                     992  |   .0077905   .2838518     0.03   0.978    -.5487246    .5643055
                     993  |  -.0085179   .3336962    -0.03   0.980     -.662757    .6457213
                     994  |    .141654   .3380593     0.42   0.675    -.5211393    .8044473
                     995  |   .8935893   .2924673     3.06   0.002     .3201829    1.466996
                     996  |   .4729184   .2798644     1.69   0.091     -.075779    1.021616
                     997  |  -.1059686   .3329282    -0.32   0.750    -.7587021     .546765
                     998  |   .2323011   .2813375     0.83   0.409    -.3192846    .7838867
                     999  |   .5899104   .3478565     1.70   0.090    -.0920913    1.271912
                    1000  |   .9079521   .2721326     3.34   0.001     .3744135    1.441491
                    1001  |   .9025418   .2966969     3.04   0.002     .3208429    1.484241
                    1002  |   .5078207   .2625959     1.93   0.053    -.0070203    1.022662
                    1003  |   .5049042   .2759623     1.83   0.067    -.0361428    1.045951
                    1004  |     .49593   .2793704     1.78   0.076     -.051799    1.043659
                    1005  |   .4392211   .2939808     1.49   0.135    -.1371528    1.015595
                    1006  |   .5125398   .2997497     1.71   0.087    -.0751444    1.100224
                    1007  |   .2076206    .273505     0.76   0.448    -.3286087    .7438498
                    1008  |    .214383   .2953374     0.73   0.468    -.3646505    .7934165
                    1009  |   .8191737   .2737513     2.99   0.003     .2824616    1.355886
                    1010  |   .4811052   .2726698     1.76   0.078    -.0534867    1.015697
                    1011  |   .1148891   .2667568     0.43   0.667    -.4081097    .6378879
                    1012  |   .3135445   .2830223     1.11   0.268    -.2413443    .8684334
                    1013  |   .4493406   .2772284     1.62   0.105    -.0941888      .99287
                          |
               1.positive |  -.0029739   .2689561    -0.01   0.991    -.5302848     .524337
                          |
event_days_ada_M#positive |
                   985 1  |  -.8453286   .3939001    -2.15   0.032    -1.617602   -.0730546
                   986 1  |  -.9538194   .3682564    -2.59   0.010    -1.675817   -.2318221
                   987 1  |  -.2412497   .3938762    -0.61   0.540    -1.013477    .5309773
                   988 1  |  -.4253707   .5270315    -0.81   0.420     -1.45866    .6079185
                   989 1  |  -.3164777   .4162356    -0.76   0.447    -1.132542     .499587
                   990 1  |  -.5351871   .4024553    -1.33   0.184    -1.324234    .2538601
                   991 1  |   .0054985   .3892786     0.01   0.989    -.7577146    .7687116
                   992 1  |   .4417489   .3858793     1.14   0.252    -.3147996    1.198297
                   993 1  |  -.3983888   .4334559    -0.92   0.358    -1.248215    .4514376
                   994 1  |  -.4940214   .4371155    -1.13   0.258    -1.351023    .3629798
                   995 1  |  -1.030903   .3916607    -2.63   0.009    -1.798786    -.263019
                   996 1  |  -.5186658   .3710412    -1.40   0.162    -1.246123    .2087913
                   997 1  |   .2708424   .4153194     0.65   0.514    -.5434258    1.085111
                   998 1  |  -.4063028   .3765717    -1.08   0.281    -1.144603    .3319973
                   999 1  |   .0263836   .4397345     0.06   0.952    -.8357526    .8885198
                  1000 1  |  -.4886616   .3486664    -1.40   0.161    -1.172251     .194928
                  1001 1  |  -.2962188   .3758288    -0.79   0.431    -1.033062     .440625
                  1002 1  |  -.0852981   .3466509    -0.25   0.806    -.7649361    .5943398
                  1003 1  |  -.1267391   .3600917    -0.35   0.725    -.8327289    .5792506
                  1004 1  |  -.1439052   .3603799    -0.40   0.690      -.85046    .5626496
                  1005 1  |  -.2405036   .3746136    -0.64   0.521    -.9749647    .4939574
                  1006 1  |  -.1230628   .3790764    -0.32   0.745    -.8662737    .6201481
                  1007 1  |  -.0413649    .348249    -0.12   0.905     -.724136    .6414062
                  1008 1  |  -.3359123   .3815236    -0.88   0.379    -1.083921    .4120965
                  1009 1  |  -.4877408   .3545615    -1.38   0.169    -1.182888    .2074064
                  1010 1  |  -.4793303   .3747856    -1.28   0.201    -1.214129    .2554681
                  1011 1  |   .1044319   .3534859     0.30   0.768    -.5886065    .7974704
                  1012 1  |  -.2265976   .3682564    -0.62   0.538    -.9485949    .4953996
                  1013 1  |  -.1547984   .3596772    -0.43   0.667    -.8599756    .5503787
                          |
                    _cons |   2.623288   .2056357    12.76   0.000     2.220122    3.026454
-------------------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86           0          86     |
-----------------------------------------------------+

. preserve

.         regsave, ci

.         gen days = _n

.         drop if coef==0 & days>29
(33 observations deleted)

.         /*we drop in 29 instead of 30 because of extra 0*/
.         drop days

.         gen days = _n

.         set obs `=_N+1' 
number of observations (_N) was 60, now 61

.         replace coef = 0 in 30
(1 real change made)

.         replace coef = 0 in 60
(1 real change made)

.         replace days = 60 in 60
(0 real changes made)

.         gen menOt = (days>30)

.         replace days = days-30 if menOt
(30 real changes made)

. 
.         forvalues var =1(1)30 {
  2.                 sum coef if days==`var' & !menOt
  3.                 replace coef = coef+r(mean) if menOt & days==`var'
  4.         }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.8045159           .  -.8045159  -.8045159
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.5074423           .  -.5074423  -.5074423
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2850101           .   .2850101   .2850101
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .572883           .    .572883    .572883
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5230712           .   .5230712   .5230712
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .393895           .    .393895    .393895
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2363167           .   .2363167   .2363167
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .0077905           .   .0077905   .0077905
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.0085179           .  -.0085179  -.0085179
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .141654           .    .141654    .141654
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .8935893           .   .8935893   .8935893
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4729184           .   .4729184   .4729184
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1059686           .  -.1059686  -.1059686
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2323011           .   .2323011   .2323011
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5899104           .   .5899104   .5899104
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .9079521           .   .9079521   .9079521
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .9025418           .   .9025418   .9025418
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5078207           .   .5078207   .5078207
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5049043           .   .5049043   .5049043
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1      .49593           .     .49593     .49593
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4392211           .   .4392211   .4392211
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .5125398           .   .5125398   .5125398
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2076206           .   .2076206   .2076206
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     .214383           .    .214383    .214383
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .8191738           .   .8191738   .8191738
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4811052           .   .4811052   .4811052
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .1148891           .   .1148891   .1148891
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .3135445           .   .3135445   .3135445
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .4493406           .   .4493406   .4493406
(1 real change made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1           0           .          0          0
(0 real changes made)

. 
.         replace days = days-15
(60 real changes made)

.         sum coef if days==-1 & !menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .2323011           .   .2323011   .2323011

.         gen coef_n = coef-r(mean) if !menOt
(31 missing values generated)

. 
.         sum coef if days==-1 & menOt

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1   -.1740017           .  -.1740017  -.1740017

.         replace coef_n = coef-r(mean) if menOt
(30 real changes made)

. 
.         graph twoway (lpoly coef_n days if menOt & days<0, lp(dash) bwidth(6) color(red) msize(small)) (lpoly coef_n days if menOt &
>  days>0, lp(dash) bwidth(6) color(red) msize(small)) (lpoly coef_n days if !menOt & days<0, bwidth(6) lp(line) color(eltgreen) msize
> (small)) (lpoly coef_n days if !menOt & days>0, bwidth(6) lp(line) color(eltgreen) msize(small)) (lpolyci coef_n days if menOt & day
> s<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(red%20)) (lpolyci coef_n days if menOt & days>0, bwidth(7) pwidth(7) level(90)
>  lwidth(none) color(red%20)) (lpolyci coef_n days if !menOt & days>0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20))
>  (lpolyci coef_n days if !menOt & days<0, bwidth(7) pwidth(7) level(90) lwidth(none) color(eltgreen%20))  (scatter coef_n days if !m
> enOt & coef_n>-1 & coef_n<1, msymbol(Oh) color(eltgreen) msize(small)) (scatter coef_n days if menOt & coef_n>-1 & coef_n<1, msymbol
> (X) msize(medium) graphregion(color(white)) legend( cols(1) order(1 "Positive message" 3 "Non-positive message")) ytitle("Tweet enga
> gement", size(large)) xtitle("Days since event", size(large)) lwidth(0.3 0.3) color(red) tline(0, lp(solid) lc(grey)) graphregion(lw
> idth(large)))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style line not found in class linepattern, default attributes used)
(note:  named style grey not found in class color, default attributes used)

.                 graph export "${PathFig}FigureA10.png", replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA10.png written in PNG format)

. restore

. 
end of do-file

. do "3_twitter_rightwing.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: Table A18
. 
. ********************************************************************************
. 
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. * Path 
. 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}tweetsRW.dta", clear

. 
. gen dayC_ada = date2 - 21198

. gen dayC_sq_ada = dayC_ada*dayC_ada

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada

. gen post_ada = date2>=21198

. gen dayC_post_ada = dayC_ada*post_ada

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada

. 
. 
. gen hour_sq = hour*hour

. gen hour_cu = hour*hour*hour

. 
. bysort userid: gen n_u = _n

. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21198

. foreach targetdate in 21198 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(46,760 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21198

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
. 
. 
. **** Table A18 ***
. 
. 
. eststo clear

. 
. eststo, title("Engagment"): reghdfe log_engagement post_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_a
> da , cl(userid) abs(userid)
(MWFE estimator converged in 1 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        337
Absorbing 1 HDFE group                            F(   8,      6) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.4490
                                                  Adj R-squared   =     0.4250
                                                  Within R-sq.    =     0.2851
Number of clusters (userid)  =          7         Root MSE        =     1.1150

                                 (Std. Err. adjusted for 7 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ada |   .0186459   .2597336     0.07   0.945    -.6168994    .6541913
     hour_sq |  -.0032291   .0036797    -0.88   0.414     -.012233    .0057748
     hour_cu |   .0001597   .0001595     1.00   0.355    -.0002306      .00055
  gg_hashtag |  -.3056004    .217931    -1.40   0.210    -.8388583    .2276576
       gg_at |   -1.13131   .2311811    -4.89   0.003     -1.69699   -.5656302
    gg_reply |  -1.939088   .3734655    -5.19   0.002    -2.852925    -1.02525
       gg_rt |    .095659   .1283526     0.75   0.484    -.2184084    .4097264
     gg_http |  -.0595463    .207088    -0.29   0.783    -.5662723    .4471798
       _cons |   4.615981   .3630908    12.71   0.000     3.727529    5.504432
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |         7           7           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada , cl(u
> serid) abs(userid)
(MWFE estimator converged in 1 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        337
Absorbing 1 HDFE group                            F(   8,      6) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.3830
                                                  Adj R-squared   =     0.3562
                                                  Within R-sq.    =     0.2806
Number of clusters (userid)  =          7         Root MSE        =     1.0690

                                 (Std. Err. adjusted for 7 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
      log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ada |   .0167221   .2249221     0.07   0.943    -.5336423    .5670866
     hour_sq |  -.0019249   .0031566    -0.61   0.564    -.0096487    .0057989
     hour_cu |   .0001154   .0001357     0.85   0.428    -.0002166    .0004474
  gg_hashtag |   -.292562   .2039218    -1.43   0.201    -.7915406    .2064166
       gg_at |   .2384407   .2176665     1.10   0.315    -.2941701    .7710515
    gg_reply |  -2.487767   .3215182    -7.74   0.000    -3.274493    -1.70104
       gg_rt |   .1462494   .1427448     1.02   0.345    -.2030344    .4955333
     gg_http |  -.3225112   .1386382    -2.33   0.059    -.6617466    .0167242
       _cons |   3.020096   .2003107    15.08   0.000     2.529953    3.510238
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |         7           7           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_ada hour_sq hour_cu gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada , cl(use
> rid) abs(userid)
(MWFE estimator converged in 1 iterations)
warning: missing F statistic; dropped variables due to collinearity or too few clusters

HDFE Linear regression                            Number of obs   =        337
Absorbing 1 HDFE group                            F(   8,      6) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.7678
                                                  Adj R-squared   =     0.7577
                                                  Within R-sq.    =     0.6754
Number of clusters (userid)  =          7         Root MSE        =     1.1053

                                 (Std. Err. adjusted for 7 clusters in userid)
------------------------------------------------------------------------------
             |               Robust
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_ada |  -.0584252   .1959768    -0.30   0.776    -.5379632    .4211128
     hour_sq |  -.0006426   .0036761    -0.17   0.867    -.0096376    .0083524
     hour_cu |   .0000304   .0001692     0.18   0.863    -.0003837    .0004445
  gg_hashtag |   .0577855   .1101546     0.52   0.619    -.2117532    .3273241
       gg_at |  -3.487751   .4618068    -7.55   0.000    -4.617752   -2.357751
    gg_reply |   .5304556   .3242923     1.64   0.153     -.263059     1.32397
       gg_rt |  -.1073442   .1399138    -0.77   0.472    -.4497009    .2350125
     gg_http |   .8908689    .427197     2.09   0.082    -.1544446    1.936182
       _cons |   3.680299   .6422865     5.73   0.001      2.10868    5.251917
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |         7           7           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. 
. 
. esttab using "${PathTab}TableA18.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_ada) stats(N controls, fmt(0) labels("N" "C
> ontrols")) label nodepvar mtitles title("Violent attack and Twitter engagement: Confederation support") replace nonotes postfoot(" "
> )
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA18.tex)

. 
. 
end of do-file

. do "4_survey_main.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All tables and graphs using CBOS data
. 
. ********************************************************************************
. 
. 
. *** path ***
. 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}cbos_final.dta", clear

. 
. 
. *** recode ***
. 
. label variable treat2 "Treatment"

. label define treat 1 "After attack" 0 "Before attack"

. label values treat2 treat

. 
. label variable q156 "Internet User"

. label define user 1 "Internet user" 2 "No Internet user"

. label values q156 user

. 
. rename q156 internet

. 
. replace party1=0 if party1==.
(325 real changes made)

. replace party2=0 if party2==.
(325 real changes made)

. replace party3=0 if party3==.
(325 real changes made)

. replace party4=0 if party4==.
(325 real changes made)

. replace party5=0 if party5==.
(325 real changes made)

. replace party6=0 if party6==.
(325 real changes made)

. replace party7=0 if party7==.
(325 real changes made)

. replace party8=0 if party8==.
(325 real changes made)

. replace party9=0 if party9==.
(325 real changes made)

. replace party10=0 if party10==.
(325 real changes made)

. replace party11=0 if party11==.
(325 real changes made)

. 
. replace pp1=0 if pp1==.
(290 real changes made)

. replace pp2=0 if pp2==.
(290 real changes made)

. replace pp8=0 if pp8==.
(290 real changes made)

. replace pp3=0 if pp3==.
(290 real changes made)

. replace pp4=0 if pp4==.
(290 real changes made)

. replace pp5=0 if pp5==.
(290 real changes made)

. replace pp6=0 if pp6==.
(290 real changes made)

. replace pp7=0 if pp7==.
(290 real changes made)

. 
. gen party22=1 if party2==1 | party8==1
(815 missing values generated)

. replace party22 = 0  if party22!=1
(815 real changes made)

. 
. gen pp22=1 if pp2==1 | pp8==1
(783 missing values generated)

. replace pp22 = 0  if pp22!=1
(783 real changes made)

. 
. pca party1 party2 party3 party4 party5 party6 party7 party8 party9 party10 party11

Principal components/correlation                 Number of obs    =        986
                                                 Number of comp.  =         11
                                                 Trace            =         11
    Rotation: (unrotated = principal)            Rho              =     1.0000

    --------------------------------------------------------------------------
       Component |   Eigenvalue   Difference         Proportion   Cumulative
    -------------+------------------------------------------------------------
           Comp1 |      1.31968      .207582             0.1200       0.1200
           Comp2 |       1.1121     .0592461             0.1011       0.2211
           Comp3 |      1.05285     .0165511             0.0957       0.3168
           Comp4 |       1.0363     .0151635             0.0942       0.4110
           Comp5 |      1.02114    .00651164             0.0928       0.5038
           Comp6 |      1.01463    .00126786             0.0922       0.5961
           Comp7 |      1.01336    .00251134             0.0921       0.6882
           Comp8 |      1.01085    .00412658             0.0919       0.7801
           Comp9 |      1.00672    .00136412             0.0915       0.8716
          Comp10 |      1.00536      .598362             0.0914       0.9630
          Comp11 |      .406997            .             0.0370       1.0000
    --------------------------------------------------------------------------

Principal components (eigenvectors) 

    ----------------------------------------------------------------------------------------------------------------------------
        Variable |    Comp1     Comp2     Comp3     Comp4     Comp5     Comp6     Comp7     Comp8     Comp9    Comp10    Comp11 
    -------------+--------------------------------------------------------------------------------------------------------------
          party1 |  -0.7814   -0.2180   -0.0028   -0.0430   -0.0391   -0.0075   -0.0000   -0.0081   -0.0103   -0.0050    0.5816 
          party2 |   0.5621   -0.6498   -0.0055   -0.0777   -0.0665   -0.0124    0.0000   -0.0133   -0.0166   -0.0081    0.5007 
          party3 |   0.0644    0.1331    0.0051    0.1370    0.3803    0.4530    0.7071   -0.2508   -0.1211   -0.0493    0.1719 
          party4 |   0.0559    0.1130    0.0041    0.1056    0.2378    0.1147    0.0000    0.9140   -0.1999   -0.0707    0.1513 
          party5 |   0.1461    0.4347    0.7055   -0.3889   -0.1879   -0.0302    0.0000   -0.0301   -0.0349   -0.0166    0.3196 
          party6 |   0.1027    0.2419    0.0133    0.7888   -0.4862   -0.0552    0.0000   -0.0488   -0.0514   -0.0238    0.2519 
          party7 |   0.1483    0.4477   -0.7085   -0.3672   -0.1833   -0.0296    0.0000   -0.0296   -0.0344   -0.0164    0.3225 
          party8 |   0.0698    0.1462    0.0057    0.1621    0.5588   -0.7536   -0.0000   -0.1473   -0.0991   -0.0420    0.1842 
          party9 |   0.0426    0.0839    0.0029    0.0705    0.1333    0.0457    0.0000    0.0902    0.9067   -0.3495    0.1178 
         party10 |   0.0388    0.0758    0.0026    0.0622    0.1139    0.0372   -0.0000    0.0676    0.3055    0.9301    0.1077 
         party11 |   0.0644    0.1331    0.0051    0.1370    0.3803    0.4530   -0.7071   -0.2508   -0.1211   -0.0493    0.1719 
    ----------------------------------------------------------------------------------------------------------------------------

    ---------------------------
        Variable | Unexplained 
    -------------+-------------
          party1 |           0 
          party2 |           0 
          party3 |           0 
          party4 |           0 
          party5 |           0 
          party6 |           0 
          party7 |           0 
          party8 |           0 
          party9 |           0 
         party10 |           0 
         party11 |           0 
    ---------------------------

. predict pca1
(score assumed)
(10 components skipped)

Scoring coefficients 
    sum of squares(column-loading) = 1

    ----------------------------------------------------------------------------------------------------------------------------
        Variable |    Comp1     Comp2     Comp3     Comp4     Comp5     Comp6     Comp7     Comp8     Comp9    Comp10    Comp11 
    -------------+--------------------------------------------------------------------------------------------------------------
          party1 |  -0.7814   -0.2180   -0.0028   -0.0430   -0.0391   -0.0075   -0.0000   -0.0081   -0.0103   -0.0050    0.5816 
          party2 |   0.5621   -0.6498   -0.0055   -0.0777   -0.0665   -0.0124    0.0000   -0.0133   -0.0166   -0.0081    0.5007 
          party3 |   0.0644    0.1331    0.0051    0.1370    0.3803    0.4530    0.7071   -0.2508   -0.1211   -0.0493    0.1719 
          party4 |   0.0559    0.1130    0.0041    0.1056    0.2378    0.1147    0.0000    0.9140   -0.1999   -0.0707    0.1513 
          party5 |   0.1461    0.4347    0.7055   -0.3889   -0.1879   -0.0302    0.0000   -0.0301   -0.0349   -0.0166    0.3196 
          party6 |   0.1027    0.2419    0.0133    0.7888   -0.4862   -0.0552    0.0000   -0.0488   -0.0514   -0.0238    0.2519 
          party7 |   0.1483    0.4477   -0.7085   -0.3672   -0.1833   -0.0296   -0.0000   -0.0296   -0.0344   -0.0164    0.3225 
          party8 |   0.0698    0.1462    0.0057    0.1621    0.5588   -0.7536   -0.0000   -0.1473   -0.0991   -0.0420    0.1842 
          party9 |   0.0426    0.0839    0.0029    0.0705    0.1333    0.0457    0.0000    0.0902    0.9067   -0.3495    0.1178 
         party10 |   0.0388    0.0758    0.0026    0.0622    0.1139    0.0372   -0.0000    0.0676    0.3055    0.9301    0.1077 
         party11 |   0.0644    0.1331    0.0051    0.1370    0.3803    0.4530   -0.7071   -0.2508   -0.1211   -0.0493    0.1719 
    ----------------------------------------------------------------------------------------------------------------------------

. 
. gen voteIntent = -1 if party1==1
(671 missing values generated)

. replace voteIntent = 1 if party2==1 | party8==1
(171 real changes made)

. replace voteIntent = 0 if voteIntent==.
(500 real changes made)

. 
. gen oppTV=1 if q157==1 | q157==2
(655 missing values generated)

. replace oppTV=0 if oppTV==. & q157!=.
(597 real changes made)

. label variable oppTV "Pro-Opp TV channel"

. label define inout0 1 "Pro-Opp" 0 "No Pro-Opp"

. label values oppTV inout0

. 
. gen TV=1 if q157==1 | q157==2
(655 missing values generated)

. replace TV=2 if q157==6
(126 real changes made)

. replace TV=3 if q157==3 | q157==4 | q157==5 | q157==7
(361 real changes made)

. label variable TV "TV channel"

. label define inout 1 "Pro-Opp" 2 "Neutral" 3 "Pro-Gov"

. label values TV inout

. tab TV, gen(tv)

 TV channel |      Freq.     Percent        Cum.
------------+-----------------------------------
    Pro-Opp |        331       40.46       40.46
    Neutral |        126       15.40       55.87
    Pro-Gov |        361       44.13      100.00
------------+-----------------------------------
      Total |        818      100.00

. label values tv1 inout0

. 
. recode DochOs 0=0 1 2=1 3 4=2 5 6=3 7/100=9, gen(income)
(795 differences between DochOs and income)

. tab income, gen(income)

  RECODE of |
     DochOs |
(MIESI�CZNE |
    DOCHODY |
      NETTO |
    respon) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        132       13.39       13.39
          1 |        143       14.50       27.89
          2 |        263       26.67       54.56
          3 |        251       25.46       80.02
          9 |        197       19.98      100.00
------------+-----------------------------------
      Total |        986      100.00

. 
. gen voter=1 if qq35!=.
(232 missing values generated)

. replace voter=0 if qq35==.
(232 real changes made)

. 
. 
. append using "${PathData}cbos_feb19.dta"  
(note: variable educ1 was byte, now double to accommodate using data's values)
(label user already defined)
(label inout already defined)
(label inout0 already defined)
(label treat already defined)
(label tel already defined)
(label woj already defined)

. append using "${PathData}cbos_dec18.dta"  
(label user already defined)
(label inout already defined)
(label inout0 already defined)
(label treat already defined)
(label tel already defined)
(label woj already defined)

. 
. replace wave=2 if wave==.
(986 real changes made)

. 
. tostring data, replace format(%20.0f)
data was long now str5

. 
. replace day = 9 if data=="21527"
(24 real changes made)

. replace day = 8 if data=="21526"
(42 real changes made)

. replace day = 7 if data=="21525"
(33 real changes made)

. replace day = 6 if data=="21524"
(102 real changes made)

. replace day = 5 if data=="21523"
(131 real changes made)

. replace day = 4 if data=="21522"
(152 real changes made)

. replace day = 3 if data=="21521"
(143 real changes made)

. replace day = 2 if data=="21520"
(98 real changes made)

. replace day = 1 if data=="21519"
(177 real changes made)

. replace day = 0 if data=="21518"
(93 real changes made)

. replace day = -1 if data=="21517"
(21 real changes made)

. 
. replace day = 18 if data=="21587"
(14 real changes made)

. replace day = 19 if data=="21588"
(120 real changes made)

. replace day = 20 if data=="21589"
(207 real changes made)

. replace day = 21 if data=="21590"
(104 real changes made)

. replace day = 22 if data=="21591"
(161 real changes made)

. replace day = 23 if data=="21592"
(173 real changes made)

. replace day = 24 if data=="21593"
(148 real changes made)

. replace day = 25 if data=="21594"
(89 real changes made)

. replace day = 26 if data=="21600"
(3 real changes made)

. 
. gen DoW = 1 if day==14  | day==22 | day==3
(2,607 missing values generated)

. replace DoW = 2 if day==15 | day==23 | day==4
(480 real changes made)

. replace DoW = 3 if day==16 | day==24 | day==5
(424 real changes made)

. replace DoW = 4 if day==10 | day==25 | day==17 | day==18 | day==6 | day==-1
(382 real changes made)

. replace DoW = 5 if day==11 | day==26 | day==19 | day==7 | day==0
(355 real changes made)

. replace DoW = 6 if day==12 | day==27 | day==20 | day==8 | day==1
(633 real changes made)

. replace DoW = 7 if day==13 | day==21 | day==9  | day==2
(333 real changes made)

. 
. recode DoW 1/5=0 6/7=1, gen(weekend)
(3021 differences between DoW and weekend)

. 
. replace day = day+1
(3,021 real changes made)

. 
. gen wave1 = wave==1

. gen wave3 = wave==3

. 
. gen inter = treat2*tv2
(551 missing values generated)

. 
. tab q151, gen(marital)

  M16. Jaki |
       jest |
  aktualnie |
     Pana(i |      Freq.     Percent        Cum.
------------+-----------------------------------
   Kawaler| |        177       19.07       19.07
   �onaty|m |        619       66.70       85.78
   Rozwiedz |         38        4.09       89.87
   Wdowiec| |         94       10.13      100.00
------------+-----------------------------------
      Total |        928      100.00

. 
. zval plec urodzony q151 income educ voter pp1 pp2 size4 size1 marital1 marital2 treat2 internet
(2,035 missing values generated)
(2,035 missing values generated)
(2,093 missing values generated)
(2,035 missing values generated)
(2,035 missing values generated)
(2,093 missing values generated)
(2,093 missing values generated)
(183 missing values generated)

. 
. 
. 
. 
. ***** TABLES ******
. 
. 
. *** Table 3 ***
. 
. eststo clear

. eststo, title("PCA"): teffects psmatch (pca1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2, caliper(0.5
> )

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                            pca1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.1112802   .0591987    -1.88   0.060    -.2273074    .0047471
--------------------------------------------------------------------------------------------------
(est1 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("Ordinal"): teffects psmatch (voteIntent) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2, c
> aliper(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                      voteIntent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0615059   .0357502    -1.72   0.085     -.131575    .0085631
--------------------------------------------------------------------------------------------------
(est2 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("GovSup"): teffects psmatch (party1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2, calipe
> r(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                          party1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |   .0401683   .0246639     1.63   0.103     -.008172    .0885085
--------------------------------------------------------------------------------------------------
(est3 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("OppSup"): teffects psmatch (party22) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2, calip
> er(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                         party22 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0213377   .0226978    -0.94   0.347    -.0658245    .0231491
--------------------------------------------------------------------------------------------------
(est4 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  12,   2442) =     207.55
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5243
                                                  Adj R-squared   =     0.5190
                                                  Within R-sq.    =     0.5049
                                                  Root MSE        =     0.8142

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1242077   .0638334    -1.95   0.052    -.2493808    .0009655
         tv1 |   .4590654   .0387184    11.86   0.000     .3831411    .5349897
         pp1 |   -1.10065    .039871   -27.61   0.000    -1.178834   -1.022465
        pp22 |   .8265842   .0453551    18.22   0.000     .7376457    .9155227
    internet |   -.076688   .0371028    -2.07   0.039    -.1494443   -.0039318
       educ1 |  -.0287752   .0448994    -0.64   0.522      -.11682    .0592696
       size2 |   .0116142   .0481942     0.24   0.810    -.0828915    .1061199
       size3 |   .0446447   .0475364     0.94   0.348    -.0485711    .1378606
       size4 |     .04148   .0534134     0.78   0.437    -.0632602    .1462201
     weekend |  -.0515135   .0397781    -1.30   0.195    -.1295158    .0264889
       wave1 |  -.0883668   .0555699    -1.59   0.112    -.1973357    .0206021
       wave3 |   .0788219   .0527269     1.49   0.135    -.0245722    .1822161
       _cons |    .129018   .0771374     1.67   0.095    -.0222435    .2802796
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est5 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  12,   2442) =     216.01
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5347
                                                  Adj R-squared   =     0.5296
                                                  Within R-sq.    =     0.5149
                                                  Root MSE        =     0.4781

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0624053   .0374854    -1.66   0.096    -.1359118    .0111012
         tv1 |   .2770867   .0227369    12.19   0.000      .232501    .3216724
         pp1 |  -.6135811   .0234138   -26.21   0.000     -.659494   -.5676681
        pp22 |   .5548725   .0266343    20.83   0.000     .5026444    .6071006
    internet |  -.0410488   .0217882    -1.88   0.060    -.0837741    .0016764
       educ1 |  -.0000891   .0263666    -0.00   0.997    -.0517924    .0516142
       size2 |   .0265875   .0283015     0.94   0.348    -.0289099    .0820849
       size3 |   .0330754   .0279152     1.18   0.236    -.0216645    .0878154
       size4 |   .0344577   .0313664     1.10   0.272    -.0270498    .0959652
     weekend |    -.02453   .0233593    -1.05   0.294     -.070336     .021276
       wave1 |  -.0176315   .0326328    -0.54   0.589    -.0816223    .0463592
       wave3 |   .0139655   .0309633     0.45   0.652    -.0467515    .0746825
       _cons |  -.1192245   .0452981    -2.63   0.009    -.2080512   -.0303979
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est6 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("GovSup"): reghdfe party1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  12,   2442) =     178.56
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4901
                                                  Adj R-squared   =     0.4845
                                                  Within R-sq.    =     0.4674
                                                  Root MSE        =     0.3399

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .0491022   .0266492     1.84   0.066    -.0031552    .1013596
         tv1 |  -.1602243   .0161642    -9.91   0.000    -.1919212   -.1285273
         pp1 |   .5813869   .0166454    34.93   0.000     .5487464    .6140275
        pp22 |  -.0810428   .0189349    -4.28   0.000    -.1181729   -.0439127
    internet |   .0210535   .0154897     1.36   0.174    -.0093209    .0514278
       educ1 |  -.0101523   .0187446    -0.54   0.588    -.0469093    .0266047
       size2 |  -.0115781   .0201201    -0.58   0.565    -.0510324    .0278762
       size3 |  -.0124036   .0198455    -0.63   0.532    -.0513194    .0265122
       size4 |  -.0121647   .0222991    -0.55   0.585    -.0558917    .0315624
     weekend |   .0255254   .0166066     1.54   0.124    -.0070391    .0580899
       wave1 |   .0354521   .0231993     1.53   0.127    -.0100403    .0809445
       wave3 |   -.031324   .0220125    -1.42   0.155     -.074489    .0118411
       _cons |   .1853561   .0322034     5.76   0.000     .1222073    .2485048
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est7 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("OppSup"): reghdfe party22 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  12,   2442) =     118.26
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3817
                                                  Adj R-squared   =     0.3748
                                                  Within R-sq.    =     0.3675
                                                  Root MSE        =     0.2999

------------------------------------------------------------------------------
     party22 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0133031   .0235096    -0.57   0.572     -.059404    .0327978
         tv1 |   .1168624   .0142599     8.20   0.000     .0888997    .1448251
         pp1 |  -.0321941   .0146844    -2.19   0.028    -.0609892    -.003399
        pp22 |   .4738297   .0167042    28.37   0.000     .4410739    .5065854
    internet |  -.0199954   .0136649    -1.46   0.144    -.0467913    .0068005
       educ1 |  -.0102414   .0165363    -0.62   0.536    -.0426681    .0221852
       size2 |   .0150094   .0177498     0.85   0.398    -.0197968    .0498156
       size3 |   .0206718   .0175075     1.18   0.238    -.0136593    .0550029
       size4 |    .022293    .019672     1.13   0.257    -.0162825    .0608685
     weekend |   .0009954   .0146502     0.07   0.946    -.0277326    .0297235
       wave1 |   .0178205   .0204662     0.87   0.384    -.0223124    .0579535
       wave3 |  -.0173585   .0194192    -0.89   0.371    -.0554382    .0207213
       _cons |   .0661315   .0284095     2.33   0.020     .0104223    .1218407
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est8 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. esttab using "${PathTab}Table3.tex", b(3) se(3) drop(tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3) star(* 0.1 *
> * 0.05 *** 0.01) stats(N modelN waves regionFE waveFE, fmt(0) labels("N" "Model" "Survey Waves" "Region FE" "Wave FE")) label nodepv
> ar mtitles title("Violent attack and vote intentions"} \footnotesize {) replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/Table3.tex)

. 
. 
. 
. *** Table 4 ***
. 
. 
. eststo clear

. eststo, title("PCA"): teffects psmatch (pca1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if tv2==1 & wave==2, ca
> liper(0.5)
note: tv1 omitted because of collinearity

Treatment-effects estimation                   Number of obs      =        126
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =          9
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                            pca1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |   .1592758   .1947922     0.82   0.414      -.22251    .5410615
--------------------------------------------------------------------------------------------------
(est1 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local controls = "Neutral TV"

added macro:
           e(controls) : "Neutral TV"

. eststo, title("Ordinal"): teffects psmatch (voteIntent) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if tv2==1 & w
> ave==2, caliper(0.5)
note: tv1 omitted because of collinearity

Treatment-effects estimation                   Number of obs      =        126
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =          9
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                      voteIntent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |   .1326279    .117924     1.12   0.261     -.098499    .3637547
--------------------------------------------------------------------------------------------------
(est2 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local controls = "Neutral TV"

added macro:
           e(controls) : "Neutral TV"

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if tv2==1, abs(woj)
(MWFE estimator converged in 1 iterations)
note: tv1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =        388
Absorbing 1 HDFE group                            F(  11,    361) =      14.68
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3521
                                                  Adj R-squared   =     0.3055
                                                  Within R-sq.    =     0.3090
                                                  Root MSE        =     0.8698

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |    .431195   .1809849     2.38   0.018     .0752778    .7871122
         tv1 |          0  (omitted)
         pp1 |  -.9843361   .1226217    -8.03   0.000    -1.225479   -.7431935
        pp22 |   .7520411   .1134424     6.63   0.000     .5289502    .9751321
    internet |  -.0525371   .0848784    -0.62   0.536    -.2194553    .1143812
       educ1 |   -.092705   .1144936    -0.81   0.419    -.3178631    .1324531
       size2 |  -.0678259   .1391298    -0.49   0.626    -.3414326    .2057808
       size3 |   .0343486   .1320189     0.26   0.795    -.2252741    .2939713
       size4 |  -.1003811   .1554982    -0.65   0.519    -.4061772    .2054149
     weekend |   .2165369   .1086863     1.99   0.047      .002799    .4302748
       wave1 |   .0080951   .1480387     0.05   0.956    -.2830314    .2992216
       wave3 |  -.4679191   .1532581    -3.05   0.002    -.7693099   -.1665284
       _cons |   .1169189   .1893027     0.62   0.537    -.2553556    .4891934
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est3 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local controls = "Neutral TV"

added macro:
           e(controls) : "Neutral TV"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if tv2==1, abs
> (woj)
(MWFE estimator converged in 1 iterations)
note: tv1 omitted because of collinearity

HDFE Linear regression                            Number of obs   =        388
Absorbing 1 HDFE group                            F(  11,    361) =      15.64
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3671
                                                  Adj R-squared   =     0.3215
                                                  Within R-sq.    =     0.3228
                                                  Root MSE        =     0.5036

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .2507158   .1047887     2.39   0.017     .0446429    .4567888
         tv1 |          0  (omitted)
         pp1 |  -.5442722   .0709969    -7.67   0.000    -.6838917   -.4046528
        pp22 |   .4847043   .0656822     7.38   0.000     .3555366     .613872
    internet |  -.0201971   .0491439    -0.41   0.681    -.1168413    .0764471
       educ1 |  -.0181731   .0662908    -0.27   0.784    -.1485377    .1121915
       size2 |  -.0190264   .0805549    -0.24   0.813    -.1774423    .1393895
       size3 |   .0352165   .0764378     0.46   0.645    -.1151027    .1855358
       size4 |  -.0756564   .0900321    -0.84   0.401    -.2527096    .1013969
     weekend |   .0857742   .0629284     1.36   0.174    -.0379781    .2095266
       wave1 |   .0406665   .0857131     0.47   0.635    -.1278932    .2092262
       wave3 |  -.3071927   .0887351    -3.46   0.001    -.4816952   -.1326901
       _cons |  -.1257238   .1096046    -1.15   0.252    -.3412675    .0898198
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est4 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local controls = "Neutral TV"

added macro:
           e(controls) : "Neutral TV"

. 
. eststo, title("PCA"): teffects psmatch (pca1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if tv2==0 & wave==2, ca
> liper(0.5)

Treatment-effects estimation                   Number of obs      =        692
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         24
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                            pca1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.1354471    .061513    -2.20   0.028    -.2560103   -.0148839
--------------------------------------------------------------------------------------------------
(est5 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local controls = "Partisan TV"

added macro:
           e(controls) : "Partisan TV"

. eststo, title("Ordinal"): teffects psmatch (voteIntent) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if tv2==0 & w
> ave==2, caliper(0.5)

Treatment-effects estimation                   Number of obs      =        692
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         24
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                      voteIntent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0814591    .037229    -2.19   0.029    -.1544266   -.0084915
--------------------------------------------------------------------------------------------------
(est6 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local controls = "Partisan TV"

added macro:
           e(controls) : "Partisan TV"

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if tv2==0, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,082
Absorbing 1 HDFE group                            F(  12,   2054) =     204.34
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5631
                                                  Adj R-squared   =     0.5573
                                                  Within R-sq.    =     0.5442
                                                  Root MSE        =     0.7949

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1937068   .0680395    -2.85   0.004    -.3271404   -.0602731
         tv1 |   .5662483   .0436938    12.96   0.000     .4805594    .6519371
         pp1 |  -1.046413   .0432915   -24.17   0.000    -1.131313   -.9615131
        pp22 |   .8140745   .0495978    16.41   0.000     .7168072    .9113418
    internet |  -.0707703   .0414092    -1.71   0.088    -.1519787     .010438
       educ1 |  -.0157302   .0484995    -0.32   0.746    -.1108435    .0793832
       size2 |   .0362165   .0509094     0.71   0.477    -.0636229     .136056
       size3 |    .061638   .0505729     1.22   0.223    -.0375415    .1608174
       size4 |   .0713161   .0562968     1.27   0.205    -.0390887     .181721
     weekend |  -.0999792   .0425643    -2.35   0.019    -.1834528   -.0165056
       wave1 |   -.090921   .0596238    -1.52   0.127    -.2078505    .0260085
       wave3 |   .1507243   .0558027     2.70   0.007     .0412886      .26016
       _cons |   .0312541   .0860074     0.36   0.716    -.1374167     .199925
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est7 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local controls = "Partisan TV"

added macro:
           e(controls) : "Partisan TV"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if tv2==0, abs
> (woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,082
Absorbing 1 HDFE group                            F(  12,   2054) =     210.33
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5705
                                                  Adj R-squared   =     0.5648
                                                  Within R-sq.    =     0.5513
                                                  Root MSE        =     0.4692

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1036473   .0401644    -2.58   0.010    -.1824144   -.0248802
         tv1 |   .3290284   .0257929    12.76   0.000     .2784455    .3796113
         pp1 |  -.5862238   .0255554   -22.94   0.000     -.636341   -.5361067
        pp22 |   .5556595   .0292781    18.98   0.000     .4982417    .6130773
    internet |  -.0405771   .0244442    -1.66   0.097    -.0885151     .007361
       educ1 |   .0034072   .0286297     0.12   0.905     -.052739    .0595535
       size2 |   .0420832   .0300523     1.40   0.162     -.016853    .1010194
       size3 |   .0406001   .0298536     1.36   0.174    -.0179464    .0991466
       size4 |   .0549272   .0332325     1.65   0.099    -.0102458    .1201002
     weekend |  -.0448231   .0251261    -1.78   0.075    -.0940983    .0044521
       wave1 |  -.0205726   .0351965    -0.58   0.559    -.0895972    .0484519
       wave3 |    .059642   .0329408     1.81   0.070    -.0049589    .1242429
       _cons |  -.1670601    .050771    -3.29   0.001    -.2666281   -.0674922
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est8 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local controls = "Partisan TV"

added macro:
           e(controls) : "Partisan TV"

. 
. esttab using "${PathTab}Table4.tex", b(3) se(3) drop(tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3) star(* 0.1 *
> * 0.05 *** 0.01) stats(N modelN controls, fmt(0) labels("N" "Model" "Sample")) label nodepvar mtitles title("Violent attack and vote
>  intentions (by TV viewership)} \footnotesize {") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/Table4.tex)

. 
. 
. 
. *** Table A19 ***
. 
. 
. eststo clear

. eststo, title("Reports party preferences"): reg voter treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      0.79
       Model |  .142483982         1  .142483982   Prob > F        =    0.3740
    Residual |  177.269281       984  .180151708   R-squared       =    0.0008
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  177.411765       985  .180113467   Root MSE        =    .42444

------------------------------------------------------------------------------
       voter |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0242223   .0272365    -0.89   0.374    -.0776706     .029226
       _cons |    .778291   .0203974    38.16   0.000     .7382635    .8183184
------------------------------------------------------------------------------
(est1 stored)

. estadd local controls = "No"

added macro:
           e(controls) : "No"

. esttab using "${PathTab}TableA19.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(treat2) stats(N controls, fmt(0) labels("N" "Con
> trols")) label nodepvar mtitles title("Violent attack and vote intentions") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA19.tex)

. 
. 
. 
. *** Table A22 ***
. 
. eststo clear

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(   9,    793) =     109.41
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5869
                                                  Adj R-squared   =     0.5744
                                                  Within R-sq.    =     0.5539
                                                  Root MSE        =     0.7645

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1004993   .0565474    -1.78   0.076    -.2114996     .010501
         tv1 |   .3645053   .0636425     5.73   0.000     .2395776     .489433
         pp1 |   -1.25841   .0641891   -19.60   0.000     -1.38441   -1.132409
        pp22 |   .7482116   .0754552     9.92   0.000     .6000959    .8963272
    internet |   .0060698   .0647694     0.09   0.925    -.1210699    .1332095
       educ1 |  -.0435565   .0619838    -0.70   0.482    -.1652283    .0781152
       size2 |  -.0398726   .0866093    -0.46   0.645    -.2098831    .1301379
       size3 |    .194981   .0731687     2.66   0.008     .0513538    .3386083
       size4 |   .2675395   .0783453     3.41   0.001     .1137509    .4213282
       _cons |   .0241475   .1037029     0.23   0.816    -.1794172    .2277123
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est1 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "No"

added macro:
             e(trends) : "No"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(   9,    793) =     110.37
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5905
                                                  Adj R-squared   =     0.5781
                                                  Within R-sq.    =     0.5561
                                                  Root MSE        =     0.4568

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0527055   .0337854    -1.56   0.119    -.1190248    .0136138
         tv1 |   .2111146   .0380245     5.55   0.000      .136474    .2857551
         pp1 |  -.6914278    .038351   -18.03   0.000    -.7667094   -.6161463
        pp22 |   .5545147   .0450822    12.30   0.000       .46602    .6430093
    internet |   .0116249   .0386977     0.30   0.764    -.0643372     .087587
       educ1 |  -.0062265   .0370335    -0.17   0.867    -.0789217    .0664687
       size2 |  -.0190779   .0517464    -0.37   0.712     -.120654    .0824983
       size3 |   .1135062   .0437161     2.60   0.010     .0276932    .1993191
       size4 |   .1497217   .0468089     3.20   0.001     .0578377    .2416058
       _cons |  -.1853909   .0619594    -2.99   0.003    -.3070147   -.0637672
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est2 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "No"

added macro:
             e(trends) : "No"

. eststo, title("GovSup"): reghdfe party1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(   9,    793) =      94.75
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5489
                                                  Adj R-squared   =     0.5352
                                                  Within R-sq.    =     0.5181
                                                  Root MSE        =     0.3237

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .0373648   .0239402     1.56   0.119    -.0096288    .0843585
         tv1 |    -.13016    .026944    -4.83   0.000      -.18305   -.0772699
         pp1 |   .6369116   .0271754    23.44   0.000     .5835674    .6902559
        pp22 |  -.0677983   .0319451    -2.12   0.034    -.1305053   -.0050913
    internet |  -.0099661   .0274211    -0.36   0.716    -.0637926    .0438604
       educ1 |   .0088512   .0262418     0.34   0.736    -.0426604    .0603628
       size2 |   .0061743   .0366673     0.17   0.866    -.0658022    .0781508
       size3 |  -.0652567   .0309771    -2.11   0.035    -.1260634   -.0044499
       size4 |  -.0882204   .0331687    -2.66   0.008    -.1533292   -.0231117
       _cons |     .22743   .0439042     5.18   0.000     .1412478    .3136122
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est3 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "No"

added macro:
             e(trends) : "No"

. eststo, title("OppSup"): reghdfe party22 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(   9,    793) =      53.45
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4138
                                                  Adj R-squared   =     0.3960
                                                  Within R-sq.    =     0.3776
                                                  Root MSE        =     0.2978

------------------------------------------------------------------------------
     party22 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0153407   .0220244    -0.70   0.486    -.0585738    .0278923
         tv1 |   .0809546   .0247879     3.27   0.001      .032297    .1296122
         pp1 |  -.0545162   .0250007    -2.18   0.030    -.1035917   -.0054407
        pp22 |   .4867163   .0293888    16.56   0.000     .4290274    .5444053
    internet |   .0016588   .0252268     0.07   0.948    -.0478603    .0511779
       educ1 |   .0026247   .0241418     0.11   0.913    -.0447647    .0500141
       size2 |  -.0129036   .0337331    -0.38   0.702    -.0791203    .0533131
       size3 |   .0482495   .0284982     1.69   0.091    -.0076913    .1041903
       size4 |   .0615013   .0305144     2.02   0.044     .0016028    .1213998
       _cons |   .0420391   .0403908     1.04   0.298    -.0372465    .1213247
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est4 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "No"

added macro:
             e(trends) : "No"

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend day, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  11,   2443) =     226.61
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5244
                                                  Adj R-squared   =     0.5194
                                                  Within R-sq.    =     0.5050
                                                  Root MSE        =     0.8139

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1447264   .0688035    -2.10   0.036    -.2796456   -.0098072
         tv1 |   .4584176   .0387089    11.84   0.000     .3825121    .5343232
         pp1 |  -1.100316   .0398462   -27.61   0.000    -1.178452   -1.022181
        pp22 |   .8274261   .0453286    18.25   0.000     .7385396    .9163125
    internet |  -.0732481   .0366179    -2.00   0.046    -.1450535   -.0014427
       educ1 |  -.0317031   .0414505    -0.76   0.444    -.1129848    .0495786
       size2 |   .0117221   .0480622     0.24   0.807    -.0825247    .1059689
       size3 |   .0448097   .0464924     0.96   0.335    -.0463588    .1359782
       size4 |   .0413218    .051902     0.80   0.426    -.0604546    .1430983
     weekend |  -.0398304   .0382231    -1.04   0.297    -.1147835    .0351227
         day |   .0099999   .0042485     2.35   0.019     .0016689    .0183309
       _cons |  -.0105302   .0681433    -0.15   0.877    -.1441549    .1230944
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est5 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "Yes"

added macro:
             e(trends) : "Yes"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend day, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  11,   2443) =     235.83
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5348
                                                  Adj R-squared   =     0.5299
                                                  Within R-sq.    =     0.5150
                                                  Root MSE        =     0.4780

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0744124    .040405    -1.84   0.066     -.153644    .0048192
         tv1 |   .2768623   .0227319    12.18   0.000     .2322866     .321438
         pp1 |  -.6136144   .0233998   -26.22   0.000    -.6594999    -.567729
        pp22 |   .5551477   .0266193    20.86   0.000     .5029489    .6073465
    internet |  -.0404955    .021504    -1.88   0.060    -.0826633    .0016724
       educ1 |  -.0003763   .0243419    -0.02   0.988    -.0481091    .0473565
       size2 |   .0269333   .0282246     0.95   0.340    -.0284134    .0822799
       size3 |   .0333948   .0273027     1.22   0.221    -.0201441    .0869337
       size4 |   .0348429   .0304796     1.14   0.253    -.0249255    .0946114
     weekend |  -.0233623   .0224466    -1.04   0.298    -.0673787    .0206541
         day |   .0024699   .0024949     0.99   0.322    -.0024225    .0073623
       _cons |  -.1498323   .0400173    -3.74   0.000    -.2283036   -.0713609
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est6 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "Yes"

added macro:
             e(trends) : "Yes"

. eststo, title("GovSup"): reghdfe party1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend day, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  11,   2443) =     194.77
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4900
                                                  Adj R-squared   =     0.4846
                                                  Within R-sq.    =     0.4672
                                                  Root MSE        =     0.3399

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .0508779   .0287308     1.77   0.077    -.0054614    .1072172
         tv1 |  -.1600533    .016164    -9.90   0.000    -.1917498   -.1283567
         pp1 |   .5811604   .0166389    34.93   0.000     .5485326    .6137882
        pp22 |  -.0813147   .0189282    -4.30   0.000    -.1184317   -.0441976
    internet |   .0197355   .0152909     1.29   0.197    -.0102489    .0497199
       educ1 |  -.0091676   .0173088    -0.53   0.596    -.0431091    .0247739
       size2 |  -.0114408   .0200697    -0.57   0.569    -.0507962    .0279146
       size3 |  -.0124674   .0194142    -0.64   0.521    -.0505374    .0256026
       size4 |  -.0120611   .0216731    -0.56   0.578    -.0545607    .0304385
     weekend |   .0200274   .0159611     1.25   0.210    -.0112714    .0513262
         day |  -.0035375   .0017741    -1.99   0.046    -.0070163   -.0000587
       _cons |   .2383927   .0284552     8.38   0.000     .1825939    .2941915
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est7 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "Yes"

added macro:
             e(trends) : "Yes"

. eststo, title("OppSup"): reghdfe party22 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend day, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,470
Absorbing 1 HDFE group                            F(  11,   2443) =     128.90
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3814
                                                  Adj R-squared   =     0.3748
                                                  Within R-sq.    =     0.3672
                                                  Root MSE        =     0.2999

------------------------------------------------------------------------------
     party22 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0235345   .0253491    -0.93   0.353    -.0732425    .0261735
         tv1 |    .116809   .0142614     8.19   0.000     .0888433    .1447747
         pp1 |   -.032454   .0146805    -2.21   0.027    -.0612414   -.0036666
        pp22 |    .473833   .0167003    28.37   0.000     .4410848    .5065813
    internet |    -.02076   .0134911    -1.54   0.124    -.0472151    .0056951
       educ1 |  -.0095439   .0152715    -0.62   0.532    -.0394903    .0204025
       size2 |   .0154925   .0177074     0.87   0.382    -.0192307    .0502156
       size3 |   .0209274   .0171291     1.22   0.222    -.0126616    .0545165
       size4 |   .0227818   .0191221     1.19   0.234    -.0147154    .0602791
     weekend |  -.0033349   .0140825    -0.24   0.813    -.0309497    .0242799
         day |  -.0010676   .0015653    -0.68   0.495     -.004137    .0020018
       _cons |   .0885604   .0251059     3.53   0.000     .0393294    .1377915
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est8 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. estadd local trends = "Yes"

added macro:
             e(trends) : "Yes"

. esttab using "${PathTab}TableA22.tex", b(3) se(3) drop(tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend day) star(* 0.1 ** 0.05
>  *** 0.01) stats(N modelN waves regionFE waveFE trends, fmt(0) labels("N" "Model" "Survey Waves" "Region FE" "Wave FE" "Linear trend
> ")) label nodepvar mtitles title("Violent attack and vote intentions"} \footnotesize {) replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA22.tex)

. 
. 
. **** Table A23 ****
. 
. 
. eststo clear

. eststo, title("PCA"): teffects psmatch (pca1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2 & day!=9 & d
> ay!=10, caliper(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                            pca1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.1112802   .0591987    -1.88   0.060    -.2273074    .0047471
--------------------------------------------------------------------------------------------------
(est1 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("Ordinal"): teffects psmatch (voteIntent) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2 & 
> day!=9 & day!=10, caliper(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                      voteIntent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0615059   .0357502    -1.72   0.085     -.131575    .0085631
--------------------------------------------------------------------------------------------------
(est2 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("GovSup"): teffects psmatch (party1) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2 & day!=
> 9 & day!=10, caliper(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                          party1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |   .0401683   .0246639     1.63   0.103     -.008172    .0885085
--------------------------------------------------------------------------------------------------
(est3 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("OppSup"): teffects psmatch (party22) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit) if wave==2 & day!
> =9 & day!=10, caliper(0.5)

Treatment-effects estimation                   Number of obs      =        818
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         29
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                         party22 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0213377   .0226978    -0.94   0.347    -.0658245    .0231491
--------------------------------------------------------------------------------------------------
(est4 stored)

. estadd local modelN = "Matching"

added macro:
             e(modelN) : "Matching"

. estadd local waves = "1"

added macro:
              e(waves) : "1"

. estadd local regionFE = "No"

added macro:
           e(regionFE) : "No"

. estadd local waveFE = "No"

added macro:
             e(waveFE) : "No"

. eststo, title("PCA"): reghdfe pca1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if day!=9 & day!=10, abs
> (woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,424
Absorbing 1 HDFE group                            F(  12,   2396) =     201.12
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5218
                                                  Adj R-squared   =     0.5164
                                                  Within R-sq.    =     0.5018
                                                  Root MSE        =     0.8156

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1312183   .0642202    -2.04   0.041    -.2571512   -.0052853
         tv1 |   .4606849   .0392403    11.74   0.000     .3837365    .5376332
         pp1 |  -1.094436   .0402799   -27.17   0.000    -1.173423   -1.015449
        pp22 |   .8155256   .0459135    17.76   0.000     .7254914    .9055599
    internet |  -.0743201   .0374296    -1.99   0.047    -.1477178   -.0009223
       educ1 |  -.0229975   .0451258    -0.51   0.610    -.1114872    .0654922
       size2 |   .0130044   .0486389     0.27   0.789    -.0823743    .1083832
       size3 |   .0562751   .0481645     1.17   0.243    -.0381733    .1507235
       size4 |   .0514597   .0539333     0.95   0.340     -.054301    .1572205
     weekend |  -.0628602   .0409137    -1.54   0.125    -.1430901    .0173697
       wave1 |  -.0976199   .0565547    -1.73   0.084    -.2085212    .0132813
       wave3 |   .0848735   .0529383     1.60   0.109     -.018936     .188683
       _cons |   .1257076   .0777538     1.62   0.106    -.0267642    .2781793
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est5 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if day!=9 & da
> y!=10, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,424
Absorbing 1 HDFE group                            F(  12,   2396) =     209.30
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5320
                                                  Adj R-squared   =     0.5267
                                                  Within R-sq.    =     0.5118
                                                  Root MSE        =     0.4787

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0661967   .0376948    -1.76   0.079    -.1401145     .007721
         tv1 |   .2789188   .0230325    12.11   0.000     .2337531    .3240845
         pp1 |  -.6096371   .0236428   -25.79   0.000    -.6559994   -.5632747
        pp22 |   .5473392   .0269494    20.31   0.000     .4944926    .6001858
    internet |  -.0396916   .0219697    -1.81   0.071    -.0827732      .00339
       educ1 |   .0036348   .0264871     0.14   0.891    -.0483052    .0555748
       size2 |   .0270823   .0285492     0.95   0.343    -.0289013     .083066
       size3 |   .0411191   .0282707     1.45   0.146    -.0143185    .0965566
       size4 |   .0403157   .0316567     1.27   0.203    -.0217617    .1023931
     weekend |  -.0308157   .0240148    -1.28   0.200    -.0779075    .0162762
       wave1 |  -.0225627   .0331954    -0.68   0.497    -.0876575     .042532
       wave3 |   .0175606   .0310727     0.57   0.572    -.0433715    .0784928
       _cons |  -.1220784   .0456385    -2.67   0.008    -.2115734   -.0325834
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est6 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("GovSup"): reghdfe party1 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if day!=9 & day!=10
> , abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,424
Absorbing 1 HDFE group                            F(  12,   2396) =     174.02
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4893
                                                  Adj R-squared   =     0.4836
                                                  Within R-sq.    =     0.4657
                                                  Root MSE        =     0.3406

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .0504784    .026817     1.88   0.060    -.0021085    .1030653
         tv1 |  -.1625838   .0163859    -9.92   0.000    -.1947158   -.1304518
         pp1 |   .5777362     .01682    34.35   0.000     .5447529    .6107195
        pp22 |  -.0807018   .0191725    -4.21   0.000    -.1182982   -.0431054
    internet |   .0206243   .0156298     1.32   0.187     -.010025    .0512736
       educ1 |   -.012974   .0188436    -0.69   0.491    -.0499253    .0239774
       size2 |   -.012748   .0203106    -0.63   0.530    -.0525761    .0270801
       size3 |  -.0168704   .0201124    -0.84   0.402      -.05631    .0225692
       size4 |  -.0164954   .0225214    -0.73   0.464    -.0606588    .0276679
     weekend |   .0268124   .0170847     1.57   0.117    -.0066899    .0603147
       wave1 |   .0362637    .023616     1.54   0.125    -.0100463    .0825737
       wave3 |  -.0334459   .0221059    -1.51   0.130    -.0767945    .0099027
       _cons |   .1901452   .0324683     5.86   0.000     .1264763    .2538141
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est7 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. eststo, title("OppSup"): reghdfe party22 treat2 tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3 if day!=9 & day!=1
> 0, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,424
Absorbing 1 HDFE group                            F(  12,   2396) =     112.91
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3748
                                                  Adj R-squared   =     0.3678
                                                  Within R-sq.    =     0.3612
                                                  Root MSE        =     0.3001

------------------------------------------------------------------------------
     party22 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0157183   .0236333    -0.67   0.506    -.0620622    .0306256
         tv1 |    .116335   .0144406     8.06   0.000     .0880176    .1446523
         pp1 |  -.0319009   .0148232    -2.15   0.031    -.0609685   -.0028333
        pp22 |   .4666374   .0168964    27.62   0.000     .4335043    .4997704
    internet |  -.0190673   .0137743    -1.38   0.166     -.046078    .0079434
       educ1 |  -.0093392   .0166065    -0.56   0.574    -.0419038    .0232254
       size2 |   .0143343   .0178994     0.80   0.423    -.0207655    .0494342
       size3 |   .0242486   .0177248     1.37   0.171    -.0105088    .0590061
       size4 |   .0238203   .0198477     1.20   0.230    -.0151002    .0627407
     weekend |  -.0040033   .0150564    -0.27   0.790    -.0335283    .0255217
       wave1 |    .013701   .0208124     0.66   0.510    -.0271112    .0545131
       wave3 |  -.0158853   .0194815    -0.82   0.415    -.0540876    .0223171
       _cons |   .0680668   .0286138     2.38   0.017     .0119565    .1241771
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est8 stored)

. estadd local modelN = "OLS"

added macro:
             e(modelN) : "OLS"

. estadd local waves = "3"

added macro:
              e(waves) : "3"

. estadd local regionFE = "Yes"

added macro:
           e(regionFE) : "Yes"

. estadd local waveFE = "Yes"

added macro:
             e(waveFE) : "Yes"

. esttab using "${PathTab}TableA23.tex", b(3) se(3) drop(tv1 pp1 pp22 internet educ1 size2 size3 size4 weekend wave1 wave3) star(* 0.1
>  ** 0.05 *** 0.01) stats(N modelN waves regionFE waveFE, fmt(0) labels("N" "Model" "Survey Waves" "Region FE" "Wave FE")) label node
> pvar mtitles title("Violent attack and vote intentions"} \footnotesize {) replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA23.tex)

. 
. 
. 
. *** Table A24 ***
. 
. 
. eststo clear

. eststo, title("PCA"): reghdfe pca1 treat2 tv2 inter tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(  11,    791) =      95.38
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.6020
                                                  Adj R-squared   =     0.5889
                                                  Within R-sq.    =     0.5701
                                                  Root MSE        =     0.7514

------------------------------------------------------------------------------
        pca1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.1451495   .0605056    -2.40   0.017    -.2639201   -.0263789
         tv2 |   .2425548   .1108042     2.19   0.029     .0250497      .46006
       inter |   .3381048   .1501397     2.25   0.025     .0433854    .6328242
         tv1 |   .5132783   .0691064     7.43   0.000     .3776246    .6489319
         pp1 |  -1.184755   .0645769   -18.35   0.000    -1.311517   -1.057992
        pp22 |   .7074621   .0748547     9.45   0.000     .5605248    .8543995
    internet |   .0170261   .0637094     0.27   0.789    -.1080334    .1420856
       educ1 |  -.0534123   .0609538    -0.88   0.381    -.1730627    .0662381
       size2 |  -.0252584   .0851914    -0.30   0.767    -.1924864    .1419696
       size3 |   .1943278   .0719555     2.70   0.007     .0530815    .3355742
       size4 |   .2732674   .0770125     3.55   0.000     .1220943    .4244404
       _cons |  -.1023246   .1070982    -0.96   0.340    -.3125549    .1079057
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est1 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Ordinal"): reghdfe voteIntent treat2 tv2 inter tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(  11,    791) =      95.50
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.6038
                                                  Adj R-squared   =     0.5908
                                                  Within R-sq.    =     0.5705
                                                  Root MSE        =     0.4499

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0854638   .0362243    -2.36   0.019     -.156571   -.0143566
         tv2 |   .1010423   .0663378     1.52   0.128    -.0291766    .2312612
       inter |   .2365758   .0898877     2.63   0.009     .0601292    .4130223
         tv1 |   .2907241   .0413736     7.03   0.000     .2095092    .3719391
         pp1 |  -.6511078   .0386618   -16.84   0.000    -.7269996    -.575216
        pp22 |   .5340362    .044815    11.92   0.000     .4460658    .6220066
    internet |   .0172693   .0381424     0.45   0.651     -.057603    .0921416
       educ1 |  -.0115238   .0364927    -0.32   0.752    -.0831577    .0601101
       size2 |  -.0102251   .0510035    -0.20   0.841    -.1103434    .0898932
       size3 |   .1123321   .0430793     2.61   0.009     .0277689    .1968954
       size4 |   .1530034   .0461069     3.32   0.001     .0624971    .2435097
       _cons |  -.2482575    .064119    -3.87   0.000     -.374121   -.1223941
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est2 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("GovSup"): reghdfe party1 treat2 tv2 inter tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(  11,    791) =      83.92
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.5680
                                                  Adj R-squared   =     0.5538
                                                  Within R-sq.    =     0.5385
                                                  Root MSE        =     0.3171

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |   .0492331   .0255363     1.93   0.054    -.0008937      .09936
         tv2 |  -.1471307   .0467647    -3.15   0.002    -.2389283   -.0553331
       inter |   -.102208   .0633661    -1.61   0.107    -.2265937    .0221777
         tv1 |  -.2014518   .0291662    -6.91   0.000    -.2587041   -.1441994
         pp1 |   .6025908   .0272545    22.11   0.000     .5490911    .6560906
        pp22 |  -.0468452   .0315923    -1.48   0.139    -.1088598    .0151693
    internet |  -.0154511   .0268884    -0.57   0.566    -.0682322      .03733
       educ1 |    .013549   .0257254     0.53   0.599    -.0369492    .0640472
       size2 |    .000281   .0359549     0.01   0.994    -.0702972    .0708592
       size3 |  -.0658297   .0303687    -2.17   0.030    -.1254425    -.006217
       size4 |  -.0907323    .032503    -2.79   0.005    -.1545346   -.0269301
       _cons |   .2932043   .0452006     6.49   0.000      .204477    .3819315
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est3 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("OppSup"): reghdfe party22 treat2 tv2 inter tv1 pp1 pp22 internet educ1 size2 size3 size4 if wave==2, abs(woj)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        818
Absorbing 1 HDFE group                            F(  11,    791) =      44.43
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.4178
                                                  Adj R-squared   =     0.3987
                                                  Within R-sq.    =     0.3819
                                                  Root MSE        =     0.2971

------------------------------------------------------------------------------
     party22 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      treat2 |  -.0362307   .0239233    -1.51   0.130    -.0831914      .01073
         tv2 |  -.0460884   .0438108    -1.05   0.293    -.1320877    .0399109
       inter |   .1343678   .0593637     2.26   0.024     .0178388    .2508968
         tv1 |   .0892724    .027324     3.27   0.001     .0356363    .1429084
         pp1 |  -.0485169    .025533    -1.90   0.058    -.0986374    .0016036
        pp22 |    .487191   .0295968    16.46   0.000     .4290934    .5452885
    internet |   .0018182     .02519     0.07   0.942    -.0476291    .0512654
       educ1 |   .0020252   .0241005     0.08   0.933    -.0452833    .0493337
       size2 |  -.0099441   .0336838    -0.30   0.768    -.0760643    .0561762
       size3 |   .0465024   .0284505     1.63   0.103     -.009345    .1023498
       size4 |   .0622711   .0304499     2.05   0.041     .0024988    .1220433
       _cons |   .0449467   .0423455     1.06   0.289    -.0381762    .1280696
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
-----------------------------------------------------+
(est4 stored)

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. 
. esttab using "${PathTab}TableA24.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(treat2 tv2 inter) stats(N controls, fmt(0) label
> s("N" "Controls")) label nodepvar mtitles title("Violent attack and vote intentions") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA24.tex)

. 
. 
. 
. 
. 
. ******* FIGURES *******
. 
. 
. **** Figure 5 ****
. 
. fvset base 14 day

. reghdfe voteIntent i.day , abs(woj weekend)
(MWFE estimator converged in 3 iterations)
note: 27.day omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,021
Absorbing 2 HDFE groups                           F(  26,   2978) =       1.68
                                                  Prob > F        =     0.0171
                                                  R-squared       =     0.0504
                                                  Adj R-squared   =     0.0370
                                                  Within R-sq.    =     0.0144
                                                  Root MSE        =     0.6644

------------------------------------------------------------------------------
  voteIntent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         day |
          0  |   -.038835    .412828    -0.09   0.925     -.848292     .770622
          1  |  -.0454233   .3917221    -0.12   0.908    -.8134968    .7226501
          2  |   .1148895   .0822047     1.40   0.162    -.0462943    .2760733
          3  |   .1111855   .0931473     1.19   0.233     -.071454    .2938251
          4  |   .1176541   .3897727     0.30   0.763    -.6465969    .8819052
          5  |  -.0016232   .3893762    -0.00   0.997    -.7650967    .7618503
          6  |   -.028391   .3897349    -0.07   0.942     -.792568     .735786
          7  |  -.1703337   .3911709    -0.44   0.663    -.9373262    .5966589
          8  |    .149261   .4024492     0.37   0.711    -.6398457    .9383677
          9  |   .2495669   .1215991     2.05   0.040     .0111402    .4879936
         10  |   .4008569    .151245     2.65   0.008     .1043016    .6974123
         11  |   .0058802   .4232969     0.01   0.989    -.8241038    .8358643
         12  |   .0279025   .3910322     0.07   0.943    -.7388182    .7946233
         13  |   .1666767   .0797978     2.09   0.037     .0102123    .3231411
         15  |  -.0812367   .3906809    -0.21   0.835    -.8472685    .6847952
         16  |  -.0917409    .389336    -0.24   0.814    -.8551357    .6716539
         17  |  -.0211087   .3894837    -0.05   0.957    -.7847931    .7425757
         18  |   .0330978   .3895756     0.08   0.932    -.7307669    .7969624
         19  |   .2979551   .4253594     0.70   0.484     -.536073    1.131983
         20  |  -.1085884   .3902865    -0.28   0.781     -.873847    .6566701
         21  |   .0600879   .0796589     0.75   0.451    -.0961041      .21628
         22  |   .1221655   .0916646     1.33   0.183    -.0575668    .3018978
         23  |  -.0920698   .3891379    -0.24   0.813    -.8550762    .6709367
         24  |  -.0295932   .3886598    -0.08   0.939    -.7916621    .7324757
         25  |  -.1767463   .3895135    -0.45   0.650    -.9404892    .5869965
         26  |   -.000963    .392136    -0.00   0.998    -.7698479    .7679219
         27  |          0  (omitted)
             |
       _cons |  -.1594835   .2630777    -0.61   0.544    -.6753159     .356349
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
     weekend |         2           1           1     |
-----------------------------------------------------+

. preserve        

.         regsave, ci

.         gen days = _n

.         replace days = days-15
(29 real changes made)

.         drop if days>13
(1 observation deleted)

.         drop if days==0
(1 observation deleted)

.         graph twoway (lpoly coef days if days<0, lp(dash) bwidth(7) color(black) msize(small)) (lpolyci coef days if days<0, pwidth(
> 7) level(90) bwidth(7) lwidth(none) color(gray%20)) (lpolyci coef days if days>0, pwidth(7) level(90) bwidth(7) lwidth(none) color(g
> ray%20)) (scatter coef days, msymbol(triangle) color(black) msize(small)) (lpoly coef days if days>0, lp(dash) bwidth(7) color(gray)
>  msize(small) graphregion(color(white)) legend(off) ytitle("Opp-vs-Gov Support", size(large)) xtitle("Survey-days since event", size
> (large)) lwidth(0.3 0.3) color(black) tline(0.5, lwidth(23) lp(solid) lc(gray%10)) tline(0.5, lp(solid) lc(black)) graphregion(lwidt
> h(large)))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

.         graph export "${PathFig}Figure5.png", as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure5.png written in PNG format)

. restore

. 
. 
. 
. *** Figure A12 ***
. 
. 
. eststo clear

. eststo Gender: reg  z_plec z_treat2 if wave==2 

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      2.22
       Model |  2.21909462         1  2.21909462   Prob > F        =    0.1364
    Residual |  982.780888       984  .998761065   R-squared       =    0.0023
-------------+----------------------------------   Adj R-squared   =    0.0012
       Total |  984.999983       985  .999999983   Root MSE        =    .99938

------------------------------------------------------------------------------
      z_plec |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |   .0477641   .0320438     1.49   0.136     -.015118    .1106462
       _cons |  -.0038709   .0319325    -0.12   0.904    -.0665346    .0587928
------------------------------------------------------------------------------

. eststo Age:  reg  z_urodzony z_treat2 if wave==2 

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      1.14
       Model |  1.14398284         1  1.14398284   Prob > F        =    0.2850
    Residual |  983.856022       984  .999853681   R-squared       =    0.0012
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  985.000005       985  1.00000001   Root MSE        =    .99993

------------------------------------------------------------------------------
  z_urodzony |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |   .0342944   .0320613     1.07   0.285    -.0286221    .0972109
       _cons |  -.0027793     .03195    -0.09   0.931    -.0654773    .0599186
------------------------------------------------------------------------------

. eststo Married: reg  z_marital2 z_treat2 if wave==2 

      Source |       SS           df       MS      Number of obs   =       928
-------------+----------------------------------   F(1, 926)       =      0.20
       Model |  .196959461         1  .196959461   Prob > F        =    0.6574
    Residual |  926.803056       926  1.00086723   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =   -0.0009
       Total |  927.000016       927  1.00000002   Root MSE        =    1.0004

------------------------------------------------------------------------------
  z_marital2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0145981   .0329075    -0.44   0.657      -.07918    .0499839
       _cons |   .0004756   .0328583     0.01   0.988    -.0640098    .0649611
------------------------------------------------------------------------------

. eststo Income: reg  z_income z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      0.49
       Model |  .485328873         1  .485328873   Prob > F        =    0.4863
    Residual |  984.514622       984  1.00052299   R-squared       =    0.0005
-------------+----------------------------------   Adj R-squared   =   -0.0005
       Total |  984.999951       985   .99999995   Root MSE        =    1.0003

------------------------------------------------------------------------------
    z_income |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0223373   .0320721    -0.70   0.486    -.0852749    .0406002
       _cons |   .0018103   .0319607     0.06   0.955    -.0609086    .0645292
------------------------------------------------------------------------------

. eststo No_education: reg  z_educ z_treat2 if wave==2 

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      3.41
       Model |   4.8445386         1   4.8445386   Prob > F        =    0.0651
    Residual |   1397.9144       984  1.42064472   R-squared       =    0.0035
-------------+----------------------------------   Adj R-squared   =    0.0024
       Total |  1402.75894       985  1.42412075   Root MSE        =    1.1919

------------------------------------------------------------------------------
     z_educ1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0705732    .038217    -1.85   0.065    -.1455693    .0044229
       _cons |   .4593968   .0380842    12.06   0.000     .3846611    .5341325
------------------------------------------------------------------------------

. eststo Village: reg  z_size1 z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =     20.63
       Model |   20.367137         1   20.367137   Prob > F        =    0.0000
    Residual |  971.229769       984  .987022123   R-squared       =    0.0205
-------------+----------------------------------   Adj R-squared   =    0.0195
       Total |  991.596906       985  1.00669737   Root MSE        =    .99349

------------------------------------------------------------------------------
     z_size1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.1447033    .031855    -4.54   0.000    -.2072148   -.0821919
       _cons |   .0270516   .0317443     0.85   0.394    -.0352427     .089346
------------------------------------------------------------------------------

. eststo Mid_city: reg  z_size4 z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      4.98
       Model |  7.46998532         1  7.46998532   Prob > F        =    0.0258
    Residual |  1475.02716       984  1.49901134   R-squared       =    0.0050
-------------+----------------------------------   Adj R-squared   =    0.0040
       Total |  1482.49715       985  1.50507325   Root MSE        =    1.2243

------------------------------------------------------------------------------
     z_size4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |   .0876342   .0392569     2.23   0.026     .0105973     .164671
       _cons |   .2480992   .0391205     6.34   0.000     .1713299    .3248685
------------------------------------------------------------------------------

. eststo Voter: reg  z_voter z_treat2 if wave==2 

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      0.79
       Model |  .791078988         1  .791078988   Prob > F        =    0.3740
    Residual |  984.208898       984  1.00021229   R-squared       =    0.0008
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  984.999977       985  .999999977   Root MSE        =    1.0001

------------------------------------------------------------------------------
     z_voter |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0285183   .0320671    -0.89   0.374    -.0914461    .0344095
       _cons |   .0023112   .0319557     0.07   0.942     -.060398    .0650204
------------------------------------------------------------------------------

. eststo Voted_gov_15: reg  z_pp1  z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      3.90
       Model |  3.93926893         1  3.93926893   Prob > F        =    0.0486
    Residual |  994.181128       984  1.01034667   R-squared       =    0.0039
-------------+----------------------------------   Adj R-squared   =    0.0029
       Total |  998.120397       985   1.0133202   Root MSE        =    1.0052

------------------------------------------------------------------------------
       z_pp1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0636387   .0322291    -1.97   0.049    -.1268845    -.000393
       _cons |    .018602   .0321172     0.58   0.563    -.0444241    .0816281
------------------------------------------------------------------------------

. eststo Voted_opp_15: reg  z_pp2  z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       986
-------------+----------------------------------   F(1, 984)       =      1.46
       Model |    1.542498         1    1.542498   Prob > F        =    0.2266
    Residual |  1036.99574       984  1.05385746   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0005
       Total |  1038.53824       985  1.05435355   Root MSE        =    1.0266

------------------------------------------------------------------------------
       z_pp2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |   .0398223   .0329158     1.21   0.227     -.024771    .1044155
       _cons |   .0296235   .0328015     0.90   0.367    -.0347454    .0939924
------------------------------------------------------------------------------

. eststo Internet_user: reg  z_internet z_treat2 if wave==2

      Source |       SS           df       MS      Number of obs   =       928
-------------+----------------------------------   F(1, 926)       =      2.72
       Model |  2.24414324         1  2.24414324   Prob > F        =    0.0993
    Residual |  763.435418       926  .824444296   R-squared       =    0.0029
-------------+----------------------------------   Adj R-squared   =    0.0019
       Total |  765.679562       927  .825975795   Root MSE        =    .90799

------------------------------------------------------------------------------
  z_internet |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    z_treat2 |  -.0492756   .0298667    -1.65   0.099    -.1078899    .0093387
       _cons |  -.0537751   .0298221    -1.80   0.072    -.1123018    .0047516
------------------------------------------------------------------------------

. 
. coefplot Gender || Age || Married || Income || No_education || Village || Mid_city || Voter || Voted_gov_15 || Voted_opp_15 || Inter
> net_user, keep(z_treat2) xline(0) horizontal bycoefs byopts(xrescale) level(95 90) title("Balance test") nolabel legend(off) 

.         graph export "${PathFig}FigureA12.png", as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA12.png written in PNG format)

. 
. 
. 
. *** Figure A13 ***
. 
. teffects psmatch (voteIntent) (treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4, probit), caliper(0.5)

Treatment-effects estimation                   Number of obs      =      2,470
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: probit                                       max =         86
--------------------------------------------------------------------------------------------------
                                 |              AI Robust
                      voteIntent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
ATE                              |
                          treat2 |
(After attack vs Before attack)  |  -.0276235   .0198814    -1.39   0.165    -.0665904    .0113434
--------------------------------------------------------------------------------------------------

. 
. psmatch2 treat2 tv1 pp1 pp22 internet educ1 size1 size2 size4

Probit regression                               Number of obs     =      2,470
                                                LR chi2(8)        =      18.46
                                                Prob > chi2       =     0.0180
Log likelihood = -1701.2019                     Pseudo R2         =     0.0054

------------------------------------------------------------------------------
      treat2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         tv1 |   .0754666    .059275     1.27   0.203    -.0407103    .1916436
         pp1 |   .0010151   .0608277     0.02   0.987     -.118205    .1202351
        pp22 |   -.095195   .0699585    -1.36   0.174    -.2323112    .0419212
    internet |   .0259651   .0564897     0.46   0.646    -.0847527    .1366829
       educ1 |   .0372127   .0645473     0.58   0.564    -.0892977    .1637232
       size1 |  -.1183872   .0627971    -1.89   0.059    -.2414673    .0046928
       size2 |   .1370645   .0789989     1.74   0.083    -.0177705    .2918994
       size4 |   .1122235   .0836035     1.34   0.179    -.0516364    .2760834
       _cons |   .0032472   .0937606     0.03   0.972    -.1805201    .1870146
------------------------------------------------------------------------------

. pstest, both graph

----------------------------------------------------------------------------------------
                Unmatched |       Mean               %reduct |     t-test    |  V(T)/
Variable          Matched | Treated Control    %bias  |bias| |    t    p>|t| |  V(C)
--------------------------+----------------------------------+---------------+----------
tv1                    U  | .41641   .38908      5.6         |   1.38  0.167 |     .
                       M  | .41641   .41094      1.1    80.0 |   0.28  0.779 |     .
                          |                                  |               |
pp1                    U  | .30859   .31092     -0.5         |  -0.13  0.900 |     .
                       M  | .30859   .31875     -2.2  -335.8 |  -0.55  0.580 |     .
                          |                                  |               |
pp22                   U  | .20078   .21092     -2.5         |  -0.62  0.533 |     .
                       M  | .20078   .19375      1.7    30.7 |   0.45  0.655 |     .
                          |                                  |               |
internet               U  | 1.3219    1.316      1.2         |   0.30  0.762 |  1.16*
                       M  | 1.3219   1.3141      1.6   -32.2 |   0.41  0.683 |  1.17*
                          |                                  |               |
educ1                  U  | .24141    .2395      0.4         |   0.11  0.912 |     .
                       M  | .24141   .24141      0.0   100.0 |   0.00  1.000 |     .
                          |                                  |               |
size1                  U  | .36875   .43361    -13.3         |  -3.29  0.001 |     .
                       M  | .36875   .36875      0.0   100.0 |  -0.00  1.000 |     .
                          |                                  |               |
size2                  U  | .17188   .13529     10.2         |   2.52  0.012 |     .
                       M  | .17188   .16797      1.1    89.3 |   0.26  0.793 |     .
                          |                                  |               |
size4                  U  | .14219   .11681      7.6         |   1.87  0.061 |     .
                       M  | .14219   .14609     -1.2    84.6 |  -0.28  0.779 |     .
                          |                                  |               |
----------------------------------------------------------------------------------------
* if variance ratio outside [0.90; 1.12] for U and [0.90; 1.12] for M

-----------------------------------------------------------------------------------
 Sample    | Ps R2   LR chi2   p>chi2   MeanBias   MedBias      B      R     %Var
-----------+-----------------------------------------------------------------------
 Unmatched | 0.005     18.46    0.018      5.2       4.0      17.3    1.02    100
 Matched   | 0.000      0.86    0.999      1.1       1.1       3.7    1.01    100
-----------------------------------------------------------------------------------
* if B>25%, R outside [0.5; 2]

. 
. 
. 
. **** Figure A14 ****
. 
. *** pis ***
. 
. fvset base 14 day

. reghdfe party1 i.day , abs(woj weekend)
(MWFE estimator converged in 3 iterations)
note: 27.day omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,021
Absorbing 2 HDFE groups                           F(  26,   2978) =       1.20
                                                  Prob > F        =     0.2248
                                                  R-squared       =     0.0454
                                                  Adj R-squared   =     0.0319
                                                  Within R-sq.    =     0.0103
                                                  Root MSE        =     0.4569

------------------------------------------------------------------------------
      party1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         day |
          0  |   .2511997   .2839122     0.88   0.376    -.3054843    .8078837
          1  |   .2703886   .2693972     1.00   0.316    -.2578349    .7986121
          2  |  -.0266081   .0565343    -0.47   0.638    -.1374583     .084242
          3  |  -.0289426   .0640597    -0.45   0.651    -.1545484    .0966632
          4  |   .2121728   .2680565     0.79   0.429    -.3134219    .7377676
          5  |   .2626404   .2677838     0.98   0.327    -.2624196    .7877004
          6  |   .2811122   .2680305     1.05   0.294    -.2444316     .806656
          7  |   .3754221   .2690181     1.40   0.163     -.152058    .9029022
          8  |   .2708663   .2767745     0.98   0.328    -.2718222    .8135549
          9  |  -.0740743   .0836268    -0.89   0.376    -.2380464    .0898978
         10  |  -.2332891    .104015    -2.24   0.025    -.4372377   -.0293405
         11  |   .2833184    .291112     0.97   0.331    -.2874825    .8541194
         12  |   .2245577   .2689227     0.84   0.404    -.3027355    .7518509
         13  |  -.0446335    .054879    -0.81   0.416     -.152238     .062971
         15  |   .3041438   .2686811     1.13   0.258    -.2226756    .8309632
         16  |    .326413   .2677562     1.22   0.223    -.1985929    .8514188
         17  |   .2693345   .2678577     1.01   0.315    -.2558704    .7945395
         18  |   .2280193    .267921     0.85   0.395    -.2973097    .7533483
         19  |   .0578603   .2925304     0.20   0.843    -.5157218    .6314425
         20  |   .3445734   .2684099     1.28   0.199    -.1817142     .870861
         21  |  -.0308339   .0547834    -0.56   0.574    -.1382511    .0765833
         22  |  -.0953257     .06304    -1.51   0.131    -.2189321    .0282808
         23  |   .2908133     .26762     1.09   0.277    -.2339255     .815552
         24  |   .2761167   .2672911     1.03   0.302    -.2479773    .8002107
         25  |   .3043509   .2678783     1.14   0.256    -.2208944    .8295961
         26  |   .1851321   .2696818     0.69   0.492    -.3436494    .7139136
         27  |          0  (omitted)
             |
       _cons |   .1408392   .1809252     0.78   0.436    -.2139117    .4955902
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
     weekend |         2           1           1     |
-----------------------------------------------------+

. preserve        

.         regsave, ci

.         gen days = _n

.         replace days = days-15
(29 real changes made)

.         drop if days>13
(1 observation deleted)

.         drop if days==0
(1 observation deleted)

. 
.         graph twoway (lpoly coef days if days<=0, lp(dash) bwidth(7) color(gray) msize(small)) (lpoly coef days if days>0, lp(dash) 
> bwidth(7) color(gray) msize(small)) (lpolyci coef days if days<=0, pwidth(7) level(90) bwidth(7) lwidth(none) color(gray%20)) (lpoly
> ci coef days if days>0, pwidth(7) level(90) bwidth(7) lwidth(none) color(gray%20)) (scatter coef days, msymbol(triangle) msize(small
> ) graphregion(color(white)) legend(off) ytitle("Government Support", size(large)) xtitle("Survey-days since event", size(large)) lwi
> dth(0.3 0.3) color(black) tline(0.5, lwidth(23) lp(solid) lc(gray%10)) tline(0.5, lp(solid) lc(black)) graphregion(lwidth(large)))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

.         graph export "${PathFig}FigureA14a.png", as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA14a.png written in PNG format)

. restore

. 
. *** PO ***
. 
. fvset base 14 day

. reghdfe party2 i.day , abs(woj weekend)
(MWFE estimator converged in 3 iterations)
note: 27.day omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,021
Absorbing 2 HDFE groups                           F(  26,   2978) =       2.40
                                                  Prob > F        =     0.0001
                                                  R-squared       =     0.0387
                                                  Adj R-squared   =     0.0251
                                                  Within R-sq.    =     0.0205
                                                  Root MSE        =     0.3545

------------------------------------------------------------------------------
      party2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         day |
          0  |   .2087852   .2202649     0.95   0.343    -.2231016     .640672
          1  |   .2221042   .2090038     1.06   0.288    -.1877023    .6319107
          2  |   .0779823   .0438604     1.78   0.076    -.0080175    .1639822
          3  |   .0602667   .0496988     1.21   0.225    -.0371808    .1577143
          4  |   .3215946   .2079637     1.55   0.122    -.0861725    .7293616
          5  |   .2595482   .2077521     1.25   0.212     -.147804    .6669004
          6  |   .2318322   .2079435     1.11   0.265    -.1758954    .6395597
          7  |   .1961667   .2087097     0.94   0.347    -.2130631    .6053965
          8  |   .3907938   .2147273     1.82   0.069     -.030235    .8118226
          9  |   .1874044   .0648793     2.89   0.004     .0601916    .3146173
         10  |    .177625    .080697     2.20   0.028     .0193976    .3358525
         11  |   .2788455   .2258506     1.23   0.217    -.1639934    .7216845
         12  |   .2193947   .2086357     1.05   0.293    -.1896901    .6284795
         13  |   .1152285   .0425762     2.71   0.007     .0317467    .1987102
         15  |   .1958386   .2084483     0.94   0.348    -.2128786    .6045558
         16  |   .2219237   .2077307     1.07   0.285    -.1853865    .6292339
         17  |   .2480527   .2078095     1.19   0.233     -.159412    .6555174
         18  |   .2400718   .2078585     1.15   0.248     -.167489    .6476327
         19  |   .3513032    .226951     1.55   0.122    -.0936935    .7962999
         20  |   .2079037   .2082378     1.00   0.318     -.200401    .6162083
         21  |   .0224425   .0425021     0.53   0.598    -.0608939     .105779
         22  |   .0155876   .0489078     0.32   0.750    -.0803089     .111484
         23  |   .1794893    .207625     0.86   0.387    -.2276137    .5865923
         24  |   .2339506   .2073699     1.13   0.259    -.1726521    .6405534
         25  |     .12088   .2078254     0.58   0.561    -.2866159     .528376
         26  |   .1736934   .2092246     0.83   0.407    -.2365461    .5839328
         27  |          0  (omitted)
             |
       _cons |  -.0206886   .1403654    -0.15   0.883    -.2959116    .2545344
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
         woj |        16           0          16     |
     weekend |         2           1           1     |
-----------------------------------------------------+

. preserve        

.         regsave, ci

.         gen days = _n

.         replace days = days-15
(29 real changes made)

.         drop if days>13
(1 observation deleted)

.         drop if days==0
(1 observation deleted)

. 
.         graph twoway (lpoly coef days if days<=0, lp(dash) bwidth(7) color(gray) msize(small)) (lpoly coef days if days>0, lp(dash) 
> bwidth(7) color(gray) msize(small)) (lpolyci coef days if days<=0, pwidth(7) level(90) bwidth(7) lwidth(none) color(gray%20)) (lpoly
> ci coef days if days>0, pwidth(7) level(90) bwidth(7) lwidth(none) color(gray%20)) (scatter coef days, msymbol(triangle) msize(small
> ) graphregion(color(white)) legend(off) ytitle("Opposition Support", size(large)) xtitle("Survey-days since event", size(large)) lwi
> dth(0.3 0.3) color(black) tline(0.5, lwidth(23) lp(solid) lc(gray%10)) tline(0.5, lp(solid) lc(black)) graphregion(lwidth(large)))
(note:  named style large not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

.         graph export "${PathFig}FigureA14b.png", as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/FigureA14b.png written in PNG format)

. restore

. 
. 
. 
. *** Figure A18 ***
. 
. graph bar (mean) pp1 party1 pp22 party22 , over(TV)

. 
. 
. 
end of do-file

. do "5_news_main.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All graphs using news data
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. * Path 
. 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
.         use "${PathData}news_final.dta", clear

. 
. 
. ****gen date****
. gen year = substr(date,1,4)

. replace year = substr(date,7,4) if progov
(893 real changes made)

. destring year, replace
year: all characters numeric; replaced as int
(39 missing values generated)

. gen month = substr(date,6,2)
(39 missing values generated)

. replace month = substr(date,4,2) if progov
(893 real changes made)

. destring month, replace
month: all characters numeric; replaced as byte
(39 missing values generated)

. gen day = substr(date,9,2)
(39 missing values generated)

. replace day = substr(date,1,2) if progov
(876 real changes made)

. destring day, replace
day: all characters numeric; replaced as byte
(39 missing values generated)

. gen date2 = mdy(month,day,year)
(39 missing values generated)

. drop if date2 == .
(39 observations deleted)

. *one news article from april for some reason
. drop if month==4
(1 observation deleted)

. 
. **preliminary analysis**
. gen dOfWeek = mod(date2,7)

. replace dOfWeek = dOfWeek - 2
(3,181 real changes made)

. replace dOfWeek = dOfWeek + 7 if dOfWeek<1
(910 real changes made)

. 
. gen threat =  strpos(text, "nienawi") + strpos(text, "wrogo") + strpos(text, "pogard") + strpos(text, " odraza") + strpos(text, "obr
> zydzenie") + strpos(text, "agresja") + strpos(text, "hejt")  + strpos(text, "grozi")  + strpos(text, "grob")  + strpos(text, "zagroe
> n")  + strpos(text, "niebezpieczestw")  >0

. gen unity =  strpos(text, "jedno ") + strpos(text, "jednoi") + strpos(text, "solidarno") + strpos(text, "porozumienie") + strpos(tex
> t, "blisko ") + strpos(text, "bliskoi") >0

. gen criticism =  strpos(text, "krytyczn") + strpos(text, "wadliw") + strpos(text, "nieodpowiedzi") + strpos(text, "oszustw") + strpo
> s(text, "kamstw") + strpos(text, "nielegal") + strpos(text, "nieuczciw") + strpos(text, "niemoraln") + strpos(text, " mylc") + strpo
> s(text, " bedn")  >0

. gen twitter =  strpos(text, "Twitter") + strpos(text, "twitter")  >0

. 
. gen gov_mentions = strpos(text, "Sprawiedliwo") + strpos(text, "PiS") + strpos(text, "TVP") >0

. gen opp_mentions = strpos(text, "Platform") + strpos(text, "opozycj") + strpos(text, "Opozycj") + strpos(text, "PO") + strpos(text, 
> "Tusk") + strpos(text, "Schetyn") >0

. gen text_l = lower(text)

. replace text = text_l
(3,178 real changes made)

. drop text_l

. replace text = subinstr(text, "á", "a",.) 
(1 real change made)

. replace text = subinstr(text, "é", "e",.) 
(3 real changes made)

. replace text = subinstr(text, "í", "i",.) 
(0 real changes made)

. replace text = subinstr(text, "ó", "o",.) 
(1,732 real changes made)

. replace text = subinstr(text, "ú", "u",.)
(0 real changes made)

. replace text = subinstr(text, "ñ", "nh",.)
(0 real changes made)

. replace text = subinstr(text, ",", " ",.)
(3,174 real changes made)

. replace text = subinstr(text, `"""',  "", .)
(1,919 real changes made)

. replace text = subinstr(text, ".", " ",.) 
(3,177 real changes made)

. replace text = subinstr(text, "-", " ",.)
(2,831 real changes made)

. replace text = subinstr(text, "!", " ",.) 
(1,732 real changes made)

. replace text = subinstr(text, "/", " ",.)
(948 real changes made)

. replace text = subinstr(text, "…", " ",.)
(0 real changes made)

. replace text = subinstr(text, ":", " ",.)
(1,755 real changes made)

. replace text = subinstr(text, ";", " ",.)
(580 real changes made)

. replace text = subinstr(text, "#", "",.)
(1,023 real changes made)

. replace text = subinstr(text, "@", "",.)
(390 real changes made)

. forvalues i = 1(1)7 {
  2.         replace text = subinstr(text, "  ", " ",.)
  3. }
(3,181 real changes made)
(3,088 real changes made)
(1,713 real changes made)
(1,684 real changes made)
(1,684 real changes made)
(578 real changes made)
(4 real changes made)

. 
. replace gov_mentions = strpos(text," rzd") + strpos(text,"minist") + strpos(text,"tvp") + strpos(text,"premier") + strpos(text,"mora
> wieck") + strpos(text,"pisowsk") + strpos(text,"macierewicz") + strpos(text,"kaczysk") + strpos(text," msz ") + strpos(text,"smolesk
> ") + strpos(text,"misiewicz") + strpos(text,"szydo") + strpos(text,"ziobro") + strpos(text,"gowin") + strpos(text,"brudzinski") + st
> rpos(text,"drelich") + strpos(text,"sdownictw") >0
(872 real changes made)

. replace opp_mentions = strpos(text," opozycj") + strpos(text," lewac") + strpos(text,"sikorsk") + strpos(text,"schetyn") + strpos(te
> xt,"neuman") + strpos(text,"lis") + strpos(text,"tusk") + strpos(text,"platform") + strpos(text,"trzaskowski") + strpos(text,"lubnau
> er") >0
(812 real changes made)

. 
. gen gg_adamowicz = strpos(text, "adamowicz")>0

. 
. 
. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21562

. foreach targetdate in 21562 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(3,181 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21562

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
.         
. gen dayC_ada = date2 - 21562

. gen dayC_sq_ada = dayC_ada*dayC_ada

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada

. gen post_ada = date2>=21562

. gen dayC_post_ada = dayC_ada*post_ada

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada

. gen dayC_opos_ada = dayC_ada*proopp

. gen dayC_sq_opos_ada = dayC_sq_ada*proopp

. gen dayC_cu_opos_ada = dayC_cu_ada*proopp

. gen dayC_opos_post_ada = dayC_ada*proopp*post_ada

. gen dayC_sq_opos_post_ada = dayC_sq_ada*proopp*post_ada

. gen dayC_cu_opos_post_ada = dayC_cu_ada*proopp*post_ada

. gen post_opo_ada = post_ada*proopp

. gen post_gob_ada = post_ada*progov

. 
. gen event_days = date2-21562

. bysort event_days proopp neutral: gen n_DOP = _n

. 
. 
. 
. ****** FIGURES ********
. 
. 
. **** Figure A1 ****
. 
. set matsize 5000

. reg gg_adamowicz i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =     17.27
       Model |  192.413822        95  2.02540865   Prob > F        =    0.0000
    Residual |  361.874138     3,085  .117301179   R-squared       =    0.3471
-------------+----------------------------------   Adj R-squared   =    0.3270
       Total |   554.28796     3,180   .17430439   Root MSE        =    .34249

-------------------------------------------------------------------------------
 gg_adamowicz |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |   1.02e-13   .1499424     0.00   1.000    -.2939971    .2939971
       21551  |   1.02e-13   .1362816     0.00   1.000     -.267212     .267212
       21552  |   1.00e-13   .1405799     0.00   1.000    -.2756396    .2756396
       21553  |   1.01e-13   .1367754     0.00   1.000    -.2681801    .2681801
       21554  |   1.03e-13   .1468425     0.00   1.000     -.287919     .287919
       21555  |   1.03e-13    .153902     0.00   1.000    -.3017607    .3017607
       21556  |   1.00e-13   .1353821     0.00   1.000    -.2654482    .2654482
       21557  |   1.01e-13   .1349712     0.00   1.000    -.2646425    .2646425
       21558  |   1.00e-13   .1345834     0.00   1.000    -.2638821    .2638821
       21559  |   1.00e-13   .1332273     0.00   1.000    -.2612232    .2612232
       21560  |   1.01e-13   .1362816     0.00   1.000     -.267212     .267212
       21561  |   1.03e-13   .1499424     0.00   1.000    -.2939971    .2939971
       21562  |      .3125   .1483036     2.11   0.035     .0217161    .6032839
       21563  |   .6470588   .1345834     4.81   0.000     .3831767    .9109409
       21564  |   .4333333   .1362816     3.18   0.001     .1661214    .7005453
       21565  |   .4054054   .1335401     3.04   0.002      .143569    .6672419
       21566  |   .4242424   .1349712     3.14   0.002     .1595999     .688885
       21567  |   .2083333    .139822     1.49   0.136    -.0658203     .482487
       21568  |   .8235294   .1468425     5.61   0.000     .5356105    1.111448
       21569  |        .55   .1432749     3.84   0.000     .2690761    .8309239
       21570  |   .4444444   .1338694     3.32   0.001     .1819623    .7069266
       21571  |   .1891892   .1335401     1.42   0.157    -.0726473    .4510256
       21572  |      .3125   .1353821     2.31   0.021     .0470518    .5779482
       21573  |   .1666667   .1321194     1.26   0.207    -.0923842    .4257175
       21574  |    .195122   .1323769     1.47   0.141    -.0644339    .4546778
       21575  |   .3333333   .1455314     2.29   0.022     .0479851    .6186815
       21576  |   1.02e-13   .1563258     0.00   1.000    -.3065131    .3065131
       21577  |   .1351351   .1335401     1.01   0.312    -.1267013    .3969716
       21578  |   .0588235   .1345834     0.44   0.662    -.2050585    .3227056
       21579  |   .1315789   .1332273     0.99   0.323    -.1296443    .3928022
       21580  |    .027027   .1335401     0.20   0.840    -.2348094    .2888635
       21581  |   1.02e-13   .3632683     0.00   1.000    -.7122722    .7122722
              |
     1.proopp |   7.17e-13   .3632683     0.00   1.000    -.7122722    .7122722
              |
 date2#proopp |
     21550 1  |  -7.17e-13   .3929969    -0.00   1.000    -.7705621    .7705621
     21551 1  |  -7.18e-13   .3746805    -0.00   1.000    -.7346484    .7346484
     21552 1  |  -7.16e-13    .376043    -0.00   1.000    -.7373201    .7373201
     21553 1  |  -7.17e-13   .3738885    -0.00   1.000    -.7330956    .7330956
     21554 1  |  -7.19e-13   .3812881    -0.00   1.000    -.7476043    .7476043
     21555 1  |  -7.18e-13   .3840621    -0.00   1.000    -.7530434    .7530434
     21556 1  |  -7.16e-13   .3730738    -0.00   1.000    -.7314982    .7314982
     21557 1  |  -7.16e-13   .3734012    -0.00   1.000    -.7321401    .7321401
     21558 1  |   .0263158   .3721569     0.07   0.944    -.7033846    .7560161
     21559 1  |  -7.16e-13   .3740832    -0.00   1.000    -.7334774    .7334774
     21560 1  |  -7.17e-13   .3735499    -0.00   1.000    -.7324317    .7324317
     21561 1  |  -7.19e-13    .382044    -0.00   1.000    -.7490865    .7490865
     21562 1  |        .25   .3829184     0.65   0.514    -.5008008    1.000801
     21563 1  |   .2007673   .3714349     0.54   0.589    -.5275175     .929052
     21564 1  |   .1072072   .3728863     0.29   0.774    -.6239233    .8383377
     21565 1  |   .2972973    .371893     0.80   0.424    -.4318857     1.02648
     21566 1  |   .1274817   .3735817     0.34   0.733    -.6050123    .8599757
     21567 1  |   .4583333   .3747078     1.22   0.221    -.2763688    1.193035
     21568 1  |   .0514706   .3775309     0.14   0.892    -.6887667    .7917079
     21569 1  |   .3166667   .3816398     0.83   0.407    -.4316272    1.064961
     21570 1  |   .1222222   .3730043     0.33   0.743    -.6091398    .8535842
     21571 1  |    .283033   .3720114     0.76   0.447    -.4463821    1.012448
     21572 1  |   .0017857   .3728015     0.00   0.996    -.7291786      .73275
     21573 1  |   .3717949   .3731867     1.00   0.319    -.3599247    1.103514
     21574 1  |    .213969   .3743751     0.57   0.568    -.5200807    .9480186
     21575 1  |  -.0175439   .3803345    -0.05   0.963    -.7632783    .7281906
     21576 1  |   .3076923   .3882809     0.79   0.428    -.4536231    1.069008
     21577 1  |   .0315315   .3741947     0.08   0.933    -.7021645    .7652275
     21578 1  |  -7.18e-13   .3726445    -0.00   1.000    -.7306564    .7306564
     21579 1  |   .1184211   .3718993     0.32   0.750    -.6107742    .8476163
     21580 1  |   .0682111   .3751265     0.18   0.856     -.667312    .8037341
     21581 1  |  -7.17e-13   .6054471    -0.00   1.000     -1.18712     1.18712
              |
    1.neutral |   .0306653    .073662     0.42   0.677    -.1137663    .1750969
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |  -.0306653   .1699598    -0.18   0.857    -.3639111    .3025805
     21552 1  |  -.0306653   .1114365    -0.28   0.783    -.2491626     .187832
     21553 1  |  -.0306653   .1082901    -0.28   0.777    -.2429934    .1816628
     21554 1  |  -.0306653   .1291149    -0.24   0.812    -.2838251    .2224946
     21555 1  |  -.0306653   .1436071    -0.21   0.831    -.3122406      .25091
     21556 1  |  -.0306653   .1051036    -0.29   0.770    -.2367455    .1754149
     21557 1  |  -.0306653   .1052394    -0.29   0.771    -.2370117    .1756812
     21558 1  |  -.0155138   .1032154    -0.15   0.881    -.2178916    .1868641
     21559 1  |  -.0306653   .1023132    -0.30   0.764    -.2312741    .1699436
     21560 1  |  -.0306653   .1076658    -0.28   0.776    -.2417692    .1804386
     21561 1  |  -.0306653   .1363013    -0.22   0.822    -.2979158    .2365852
     21562 1  |  -.2562088   .1336318    -1.92   0.055    -.5182252    .0058076
     21563 1  |  -.0657838   .1030868    -0.64   0.523    -.2679096     .136342
     21564 1  |  -.0228221    .105172    -0.22   0.828    -.2290364    .1833921
     21565 1  |   .0566829   .1014712     0.56   0.576    -.1422751     .255641
     21566 1  |  -.0845373   .1056074    -0.80   0.423    -.2916052    .1225306
     21567 1  |   .1684088   .1117403     1.51   0.132    -.0506841    .3875017
     21568 1  |   .0208053    .131201     0.16   0.874    -.2364449    .2780555
     21569 1  |   .0693347   .1309817     0.53   0.597    -.1874855    .3261549
     21570 1  |  -.1834431   .1015561    -1.81   0.071    -.3825675    .0156814
     21571 1  |  -.0484259   .1013515    -0.48   0.633    -.2471492    .1502974
     21572 1  |  -.1173588   .1048032    -1.12   0.263    -.3228499    .0881323
     21573 1  |  -.0199126   .1005531    -0.20   0.843    -.2170705    .1772453
     21574 1  |   -.010101   .1028937    -0.10   0.922    -.2118481    .1916462
     21575 1  |  -.2528875   .1276218    -1.98   0.048    -.5031197   -.0026553
     21576 1  |   .3222759   .1486649     2.17   0.030     .0307836    .6137682
     21577 1  |  -.0769115   .1058446    -0.73   0.467    -.2844446    .1306216
     21578 1  |  -.0002031   .1047416    -0.00   0.998    -.2055733    .2051671
     21579 1  |  -.0713351   .1014409    -0.70   0.482    -.2702337    .1275634
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |  -1.02e-13   .1210894    -0.00   1.000    -.2374241    .2374241
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada &  timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatt
> er coefL event_days if timeWindow3_ada &  !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL e
> vent_days if timeWindow3_ada &  neutral & n_DOP==1, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "O
> pposition" 2 "Government" 3 "Neutral")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)
> ) xtitle("Days since event") ytitle("Adamowicz articles in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(
> solid) lc(gray))) (lpoly coefL event_days if timeWindow3_ada &  proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) 
> msize(small)) (lpoly coefL event_days if timeWindow3_ada &  proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize
> (small))  (lpoly coefL event_days if timeWindow3_ada &  !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) col
> or(navy) msize(small)) (lpoly coefL event_days if timeWindow3_ada &  !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) ms
> ymbol(S) color(navy) msize(small)) (lpoly coefL event_days if timeWindow3_ada &  !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3)
>  msymbol(S) color(green) msize(small)) (lpoly coefL event_days if timeWindow3_ada &  !progov & !proopp & n_DOP==1 & post_ada, bwidth
> (3) msymbol(S) color(green) msize(small)), name(g1, replace) nodraw
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA1.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA1.png written in PNG format)

. drop coefL

. 
. 
. **** Figure A15 ****
. 
. **opp mentions**
. set matsize 5000

. reg opp_mentions i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =      6.98
       Model |  139.621179        95  1.46969662   Prob > F        =    0.0000
    Residual |  649.856972     3,085  .210650558   R-squared       =    0.1769
-------------+----------------------------------   Adj R-squared   =    0.1515
       Total |  789.478152     3,180   .24826357   Root MSE        =    .45897

-------------------------------------------------------------------------------
 opp_mentions |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |  -.2333333   .2009345    -1.16   0.246    -.6273124    .1606457
       21551  |        -.1   .1826281    -0.55   0.584    -.4580849    .2580849
       21552  |  -.1086957    .188388    -0.58   0.564    -.4780743     .260683
       21553  |   .0172414   .1832897     0.09   0.925    -.3421409    .3766237
       21554  |   .0882353   .1967804     0.45   0.654    -.2975986    .4740692
       21555  |   .1153846   .2062407     0.56   0.576    -.2889983    .5197675
       21556  |   2.62e-14   .1814226     0.00   1.000    -.3557213    .3557213
       21557  |  -.1060606    .180872    -0.59   0.558    -.4607023    .2485811
       21558  |  -.0588235   .1803522    -0.33   0.744    -.4124461    .2947991
       21559  |  -.0789474    .178535    -0.44   0.658    -.4290069    .2711122
       21560  |   2.61e-14   .1826281     0.00   1.000    -.3580849    .3580849
       21561  |        -.1   .2009345    -0.50   0.619     -.493979     .293979
       21562  |       .125   .1987385     0.63   0.529    -.2646731    .5146731
       21563  |   .0588235   .1803522     0.33   0.744    -.2947991    .4124461
       21564  |  -.2666667   .1826281    -1.46   0.144    -.6247516    .0914182
       21565  |  -.0405405   .1789541    -0.23   0.821    -.3914218    .3103408
       21566  |  -.1060606    .180872    -0.59   0.558    -.4607023    .2485811
       21567  |  -.0833333   .1873724    -0.44   0.657    -.4507207     .284054
       21568  |   .1470588   .1967804     0.75   0.455    -.2387751    .5328927
       21569  |   2.61e-14   .1919996     0.00   1.000      -.37646      .37646
       21570  |   .0833333   .1793954     0.46   0.642    -.2684133    .4350799
       21571  |  -.1216216   .1789541    -0.68   0.497    -.4725029    .2292597
       21572  |     .03125   .1814226     0.17   0.863    -.3244713    .3869713
       21573  |   2.63e-14   .1770503     0.00   1.000    -.3471484    .3471484
       21574  |   .0121951   .1773954     0.07   0.945      -.33563    .3600202
       21575  |   .0555556   .1950234     0.28   0.776    -.3268333    .4379444
       21576  |  -.1666667   .2094887    -0.80   0.426    -.5774182    .2440849
       21577  |  -.1756757   .1789541    -0.98   0.326     -.526557    .1752056
       21578  |  -.1176471   .1803522    -0.65   0.514    -.4712697    .2359755
       21579  |  -.0789474    .178535    -0.44   0.658    -.4290069    .2711122
       21580  |  -.1216216   .1789541    -0.68   0.497    -.4725029    .2292597
       21581  |        -.5   .4868078    -1.03   0.304      -1.4545    .4545003
              |
     1.proopp |         .5   .4868078     1.03   0.304    -.4545003      1.4545
              |
 date2#proopp |
     21550 1  |   .2333333   .5266465     0.44   0.658      -.79928    1.265947
     21551 1  |  -.2076923   .5021011    -0.41   0.679    -1.192179     .776794
     21552 1  |   .0346216    .503927     0.07   0.945    -.9534449    1.022688
     21553 1  |  -.2430478   .5010398    -0.49   0.628    -1.225453    .7393575
     21554 1  |   -.254902   .5109559    -0.50   0.618     -1.25675    .7469462
     21555 1  |  -.1709402   .5146733    -0.33   0.740    -1.180077    .8381968
     21556 1  |  -.1515152   .4999481    -0.30   0.762     -1.13178    .8287496
     21557 1  |  -.0939394   .5003868    -0.19   0.851    -1.075064    .8871856
     21558 1  |  -.2306502   .4987193    -0.46   0.644    -1.208506    .7472053
     21559 1  |   .0372807   .5013007     0.07   0.941    -.9456363    1.020198
     21560 1  |       -.25    .500586    -0.50   0.618    -1.231516    .7315157
     21561 1  |  -.3210526   .5119689    -0.63   0.531    -1.324887    .6827817
     21562 1  |      -.125   .5131405    -0.24   0.808    -1.131132    .8811317
     21563 1  |  -.2109974   .4977518    -0.42   0.672    -1.186956     .764961
     21564 1  |   .0774775   .4996967     0.16   0.877    -.9022945    1.057249
     21565 1  |  -.0405405   .4983657    -0.08   0.935    -1.017703    .9366217
     21566 1  |   .0715778   .5006286     0.14   0.886    -.9100214    1.053177
     21567 1  |  -.0075758   .5021378    -0.02   0.988     -.992134    .9769825
     21568 1  |  -.2408088   .5059209    -0.48   0.634    -1.232785     .751167
     21569 1  |  -.0666667   .5114272    -0.13   0.896    -1.069439    .9361056
     21570 1  |  -.2166667   .4998549    -0.43   0.665    -1.196749    .7634155
     21571 1  |   .0660661   .4985244     0.13   0.895    -.9114072    1.043539
     21572 1  |  -.2026786   .4995831    -0.41   0.685    -1.182228    .7768707
     21573 1  |  -.1538462   .5000993    -0.31   0.758    -1.134407    .8267152
     21574 1  |  -.1485588   .5016919    -0.30   0.767    -1.132243    .8351252
     21575 1  |  -.2134503   .5096779    -0.42   0.675    -1.212793    .7858921
     21576 1  |   .0128205   .5203268     0.02   0.980    -1.007402    1.033043
     21577 1  |   .0923423   .5014501     0.18   0.854    -.8908676    1.075552
     21578 1  |   .0588235   .4993727     0.12   0.906    -.9203131     1.03796
     21579 1  |   .0233918   .4983741     0.05   0.963    -.9537868     1.00057
     21580 1  |  -.1164736   .5026988    -0.23   0.817    -1.102132    .8691847
     21581 1  |         .5   .8113464     0.62   0.538    -1.090834    2.090834
              |
    1.neutral |   .1023909   .0987129     1.04   0.300    -.0911588    .2959405
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |  -.5023909   .2277594    -2.21   0.027    -.9489663   -.0558154
     21552 1  |  -.1388565   .1493337    -0.93   0.353      -.43166     .153947
     21553 1  |  -.1196322   .1451172    -0.82   0.410    -.4041684     .164904
     21554 1  |  -.2461817    .173024    -1.42   0.155    -.5854357    .0930723
     21555 1  |  -.2440913   .1924448    -1.27   0.205    -.6214241    .1332416
     21556 1  |  -.2523909   .1408471    -1.79   0.073    -.5285544    .0237727
     21557 1  |  -.1391874   .1410291    -0.99   0.324    -.4157078     .137333
     21558 1  |  -.1344764   .1383167    -0.97   0.331    -.4056786    .1367257
     21559 1  |  -.2067768   .1371077    -1.51   0.132    -.4756084    .0620547
     21560 1  |  -.1985447   .1442806    -1.38   0.169    -.4814404     .084351
     21561 1  |  -.0932999   .1826544    -0.51   0.610    -.4514365    .2648366
     21562 1  |  -.3795648   .1790771    -2.12   0.034    -.7306872   -.0284423
     21563 1  |  -.1537517   .1381444    -1.11   0.266     -.424616    .1171126
     21564 1  |   .0466288   .1409387     0.33   0.741    -.2297144     .322972
     21565 1  |  -.1560532   .1359794    -1.15   0.251    -.4226725    .1105661
     21566 1  |   .1332994   .1415221     0.94   0.346    -.1441878    .4107866
     21567 1  |  -.2227612   .1497407    -1.49   0.137    -.5163628    .0708403
     21568 1  |   -.332783   .1758196    -1.89   0.058    -.6775184    .0119524
     21569 1  |  -.0523909   .1755257    -0.30   0.765      -.39655    .2917683
     21570 1  |  -.2551686   .1360931    -1.87   0.061    -.5220109    .0116737
     21571 1  |  -.1521978   .1358189    -1.12   0.263    -.4185025    .1141069
     21572 1  |  -.1820279   .1404445    -1.30   0.195    -.4574021    .0933462
     21573 1  |   -.166907   .1347491    -1.24   0.216    -.4311139       .0973
     21574 1  |  -.2616448   .1378856    -1.90   0.058    -.5320018    .0087122
     21575 1  |  -.1394279   .1710231    -0.82   0.415    -.4747586    .1959029
     21576 1  |   -.082783   .1992226    -0.42   0.678    -.4734054    .3078394
     21577 1  |  -.0267152   .1418401    -0.19   0.851    -.3048258    .2513954
     21578 1  |   .0331133   .1403619     0.24   0.814    -.2420988    .3083255
     21579 1  |  -.1598071   .1359387    -1.18   0.240    -.4263467    .1067325
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |         .5   .1622693     3.08   0.002     .1818332    .8181668
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatter coefL event_days
>  if timeWindow3_ada & !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL event_days if timeWin
> dow3_ada & neutral & n_DOP==1 & coefL>.2, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "Pro-Opp TV"
>  2 "Pro-Gov TV" 3 "Neutral TV")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)) xtitl
> e("Days since event") ytitle("Opp mentions in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(solid) lc(gra
> y))) (lpoly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) msize(small)) 
> (lpoly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize(small))  (lpol
> y coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msize(s
> mall)) (lpoly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) msymbol(S) color(na
> vy) msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3) msymbol(S) color(
> green) msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & post_ada, bwidth(3) msymbol(S) colo
> r(green) msize(small)), name(g1, replace) nodraw
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA15a.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA15a.png written in PNG format)

. drop coefL

. 
. **gov mentions**
. set matsize 5000

. reg gov_mentions i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =      5.04
       Model |  106.165919        95  1.11753599   Prob > F        =    0.0000
    Residual |  683.647976     3,085  .221603882   R-squared       =    0.1344
-------------+----------------------------------   Adj R-squared   =    0.1078
       Total |  789.813895     3,180  .248369149   Root MSE        =    .47075

-------------------------------------------------------------------------------
 gov_mentions |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |  -.0333333   .2060924    -0.16   0.872    -.4374256    .3707589
       21551  |  -.0333333    .187316    -0.18   0.859      -.40061    .3339434
       21552  |   .0217391   .1932238     0.11   0.910    -.3571212    .4005995
       21553  |   .1896552   .1879947     1.01   0.313    -.1789522    .5582626
       21554  |   .0882353   .2018316     0.44   0.662    -.3075027    .4839733
       21555  |   .0384615   .2115347     0.18   0.856    -.3763016    .4532247
       21556  |    -.03125   .1860796    -0.17   0.867    -.3961024    .3336024
       21557  |  -.1060606   .1855148    -0.57   0.568    -.4698057    .2576845
       21558  |   .0882353   .1849817     0.48   0.633    -.2744646    .4509352
       21559  |   .1052632   .1831179     0.57   0.565    -.2537822    .4643085
       21560  |  -2.92e-14    .187316    -0.00   1.000    -.3672767    .3672767
       21561  |   .1666667   .2060924     0.81   0.419    -.2374256    .5707589
       21562  |     -.0625     .20384    -0.31   0.759    -.4621758    .3371758
       21563  |   .0588235   .1849817     0.32   0.751    -.3038763    .4215234
       21564  |        -.2    .187316    -1.07   0.286    -.5672767    .1672767
       21565  |   .0945946   .1835478     0.52   0.606    -.2652936    .4544828
       21566  |   .0454545   .1855148     0.25   0.806    -.3182906    .4091997
       21567  |   .2083333   .1921822     1.08   0.278    -.1684846    .5851513
       21568  |   .0294118   .2018316     0.15   0.884    -.3663262    .4251498
       21569  |        .05   .1969281     0.25   0.800    -.3361235    .4361235
       21570  |   .0833333   .1840004     0.45   0.651    -.2774424     .444109
       21571  |  -.0405405   .1835478    -0.22   0.825    -.4004287    .3193477
       21572  |        .25   .1860796     1.34   0.179    -.1148524    .6148524
       21573  |  -.0238095   .1815951    -0.13   0.896     -.379869      .33225
       21574  |   .0365854   .1819491     0.20   0.841    -.3201682    .3933389
       21575  |   .0555556   .2000295     0.28   0.781     -.336649    .4477601
       21576  |  -2.93e-14   .2148662    -0.00   1.000    -.4212953    .4212953
       21577  |   .1216216   .1835478     0.66   0.508    -.2382666    .4815098
       21578  |   .2941176   .1849817     1.59   0.112    -.0685822    .6568175
       21579  |   .1578947   .1831179     0.86   0.389    -.2011506    .5169401
       21580  |   .2027027   .1835478     1.10   0.270    -.1571855    .5625909
       21581  |         .5   .4993039     1.00   0.317    -.4790017    1.479002
              |
     1.proopp |         .5   .4993039     1.00   0.317    -.4790017    1.479002
              |
 date2#proopp |
     21550 1  |  -.0916667   .5401652    -0.17   0.865    -1.150787    .9674532
     21551 1  |  -.1974359   .5149897    -0.38   0.701    -1.207193    .8123215
     21552 1  |  -.2439614   .5168625    -0.47   0.637    -1.257391    .7694682
     21553 1  |  -.5122358   .5139012    -1.00   0.319    -1.519859    .4953873
     21554 1  |  -.3660131   .5240718    -0.70   0.485    -1.393578    .6615519
     21555 1  |  -.1495726   .5278846    -0.28   0.777    -1.184614    .8854682
     21556 1  |  -.2414773   .5127814    -0.47   0.638    -1.246905    .7639503
     21557 1  |  -.1606061   .5132314    -0.31   0.754    -1.166916    .8457037
     21558 1  |  -.4829721   .5115211    -0.94   0.345    -1.485929    .5199842
     21559 1  |  -.3135965   .5141688    -0.61   0.542    -1.321744    .6945513
     21560 1  |    -.40625   .5134358    -0.79   0.429    -1.412961    .6004606
     21561 1  |  -.6403509   .5251108    -1.22   0.223    -1.669953    .3892513
     21562 1  |      .0625   .5263125     0.12   0.905    -.9694584    1.094458
     21563 1  |  -.1457801   .5105287    -0.29   0.775    -1.146791    .8552306
     21564 1  |  -.0702703   .5125236    -0.14   0.891    -1.075192    .9346518
     21565 1  |  -.3108108   .5111584    -0.61   0.543    -1.313056    .6914345
     21566 1  |  -.2178683   .5134795    -0.42   0.671    -1.224665    .7889279
     21567 1  |  -.3598485   .5150273    -0.70   0.485     -1.36968    .6499827
     21568 1  |  -.2481618   .5189075    -0.48   0.633    -1.265601    .7692775
     21569 1  |       -.25   .5245552    -0.48   0.634    -1.278513    .7785128
     21570 1  |  -.4166667   .5126859    -0.81   0.416    -1.421907    .5885736
     21571 1  |  -.1261261   .5113212    -0.25   0.805    -1.128691    .8764383
     21572 1  |  -.5357143   .5124071    -1.05   0.296    -1.540408    .4689793
     21573 1  |  -.0915751   .5129365    -0.18   0.858    -1.097307    .9141566
     21574 1  |  -.2184035     .51457    -0.42   0.671    -1.227338    .7905309
     21575 1  |  -.2134503    .522761    -0.41   0.683    -1.238445    .8115446
     21576 1  |  -.2307692   .5336832    -0.43   0.665     -1.27718    .8156413
     21577 1  |   -.204955    .514322    -0.40   0.690    -1.213403    .8034934
     21578 1  |        -.5   .5121913    -0.98   0.329     -1.50427    .5042704
     21579 1  |  -.3523392    .511167    -0.69   0.491    -1.354601     .649923
     21580 1  |  -.3455598   .5156028    -0.67   0.503    -1.356519    .6653997
     21581 1  |       -1.5   .8321731    -1.80   0.072     -3.13167    .1316695
              |
    1.neutral |  -.2411642   .1012468    -2.38   0.017    -.4396821   -.0426463
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |   .1078309   .2336058     0.46   0.644    -.3502078    .5658696
     21552 1  |  -.0063813    .153167    -0.04   0.967    -.3067009    .2939382
     21553 1  |  -.1215679   .1488423    -0.82   0.414    -.4134079    .1702722
     21554 1  |  -.0507748   .1774654    -0.29   0.775    -.3987371    .2971876
     21555 1  |   .0184922   .1973847     0.09   0.925    -.3685266    .4055109
     21556 1  |   .1224142   .1444625     0.85   0.397    -.1608383    .4056668
     21557 1  |   .1507963   .1446492     1.04   0.297    -.1328222    .4344148
     21558 1  |   .0923229   .1418672     0.65   0.515    -.1858409    .3704866
     21559 1  |   .0859011   .1406271     0.61   0.541    -.1898312    .3616333
     21560 1  |   .1257796   .1479842     0.85   0.395    -.1643779    .4159371
     21561 1  |  -.1073206    .187343    -0.57   0.567    -.4746503    .2600091
     21562 1  |   .1949686   .1836739     1.06   0.289    -.1651669    .5551041
     21563 1  |   .1002512   .1416905     0.71   0.479    -.1775661    .3780684
     21564 1  |   .2941054   .1445565     2.03   0.042     .0106687    .5775422
     21565 1  |   .0088885   .1394699     0.06   0.949    -.2645748    .2823517
     21566 1  |   .1031171   .1451549     0.71   0.478     -.181493    .3877272
     21567 1  |  -.1708728   .1535844    -1.11   0.266    -.4720109    .1302653
     21568 1  |  -.0799142   .1803328    -0.44   0.658    -.4334987    .2736704
     21569 1  |   .1411642   .1800314     0.78   0.433    -.2118293    .4941577
     21570 1  |    .018942   .1395865     0.14   0.892      -.25475     .292634
     21571 1  |   .1531334   .1393053     1.10   0.272    -.1200072    .4262739
     21572 1  |  -.0249648   .1440496    -0.17   0.862    -.3074076     .257478
     21573 1  |   .2972318    .138208     2.15   0.032     .0262428    .5682208
     21574 1  |   .1359514   .1414251     0.96   0.336    -.1413454    .4132483
     21575 1  |   .1300531   .1754132     0.74   0.459    -.2138853    .4739916
     21576 1  |   .0941054   .2043365     0.46   0.645     -.306544    .4947549
     21577 1  |   .0639871    .145481     0.44   0.660    -.2212624    .3492366
     21578 1  |  -.0886677   .1439649    -0.62   0.538    -.3709444     .193609
     21579 1  |  -.0076396   .1394282    -0.05   0.956    -.2810211    .2657419
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |         .5   .1664346     3.00   0.003     .1736661    .8263339
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatter coefL event_days
>  if timeWindow3_ada & !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL event_days if timeWin
> dow3_ada & neutral & n_DOP==1, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "Pro-Opp TV" 2 "Pro-Gov
>  TV" 3 "Neutral TV")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)) xtitle("Days sin
> ce event") ytitle("Gov mentions in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(solid) lc(gray))) (lpoly
>  coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) msize(small)) (lpoly coef
> L event_days if timeWindow3_ada & proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize(small))  (lpoly coefL eve
> nt_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msize(small)) (lpo
> ly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msize(s
> mall)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3) msymbol(S) color(green) msiz
> e(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & post_ada, bwidth(3) msymbol(S) color(green) ms
> ize(small)), name(g1, replace) nodraw 
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA15b.png", width(3600) as(png) replace
(note: file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA15b.png not found)
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA15b.png written in PNG format)

. drop coefL

. 
. 
. 
. **** Figure A16 ****
. 
. 
. **threat**
. set matsize 5000

. reg threat i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =      4.66
       Model |  83.5256894        95  .879217783   Prob > F        =    0.0000
    Residual |  581.951833     3,085  .188639168   R-squared       =    0.1255
-------------+----------------------------------   Adj R-squared   =    0.0986
       Total |  665.477523     3,180  .209269661   Root MSE        =    .43433

-------------------------------------------------------------------------------
       threat |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |  -.1166667   .1901469    -0.61   0.540     -.489494    .2561607
       21551  |  -.0166667   .1728232    -0.10   0.923    -.3555269    .3221936
       21552  |   .1413043   .1782739     0.79   0.428    -.2082433     .490852
       21553  |  -.0775862   .1734494    -0.45   0.655    -.4176742    .2625018
       21554  |   .1029412   .1862158     0.55   0.580    -.2621783    .4680607
       21555  |  -.0961538   .1951681    -0.49   0.622    -.4788265    .2865188
       21556  |   5.84e-14   .1716825     0.00   1.000    -.3366235    .3366235
       21557  |  -.0378788   .1711614    -0.22   0.825    -.3734807    .2977231
       21558  |  -.1323529   .1706696    -0.78   0.438    -.4669905    .2022846
       21559  |  -.1447368   .1689499    -0.86   0.392    -.4760026    .1865289
       21560  |   .1166667   .1728232     0.68   0.500    -.2221936    .4555269
       21561  |   .0166667   .1901469     0.09   0.930    -.3561607     .389494
       21562  |     -.0625   .1880687    -0.33   0.740    -.4312526    .3062526
       21563  |   .1911765   .1706696     1.12   0.263    -.1434611     .525814
       21564  |        .05   .1728232     0.29   0.772    -.2888603    .3888603
       21565  |   .0743243   .1693465     0.44   0.661    -.2577191    .4063677
       21566  |   .0530303   .1711614     0.31   0.757    -.2825716    .3886322
       21567  |      -.125   .1773129    -0.70   0.481    -.4726633    .2226633
       21568  |   .1617647   .1862158     0.87   0.385    -.2033548    .5268842
       21569  |        .05   .1816916     0.28   0.783    -.3062489    .4062489
       21570  |   .0277778   .1697642     0.16   0.870    -.3050845      .36064
       21571  |   .0472973   .1693465     0.28   0.780    -.2847461    .3793407
       21572  |    -.03125   .1716825    -0.18   0.856    -.3678735    .3053735
       21573  |  -.0357143   .1675449    -0.21   0.831    -.3642252    .2927966
       21574  |   .0182927   .1678715     0.11   0.913    -.3108586     .347444
       21575  |   .0277778   .1845531     0.15   0.880    -.3340816    .3896372
       21576  |  -.0833333   .1982418    -0.42   0.674    -.4720327     .305366
       21577  |   .1554054   .1693465     0.92   0.359     -.176638    .4874488
       21578  |  -.0147059   .1706696    -0.09   0.931    -.3493434    .3199316
       21579  |   .0394737   .1689499     0.23   0.815    -.2917921    .3707395
       21580  |  -.0878378   .1693465    -0.52   0.604    -.4198812    .2442056
       21581  |       -.25   .4606724    -0.54   0.587    -1.153256    .6532557
              |
     1.proopp |       -.25   .4606724    -0.54   0.587    -1.153256    .6532557
              |
 date2#proopp |
     21550 1  |   .3666667   .4983723     0.74   0.462    -.6105084    1.343842
     21551 1  |   .5166667   .4751446     1.09   0.277    -.4149651    1.448298
     21552 1  |   .2661031   .4768725     0.56   0.577    -.6689168    1.201123
     21553 1  |   .4324249   .4741403     0.91   0.362    -.4972377    1.362088
     21554 1  |   .2303922    .483524     0.48   0.634    -.7176694    1.178454
     21555 1  |   .3183761   .4870418     0.65   0.513     -.636583    1.273335
     21556 1  |   .4545455   .4731072     0.96   0.337    -.4730915    1.382182
     21557 1  |   .2712121   .4735223     0.57   0.567    -.6572388    1.199663
     21558 1  |   .4744582   .4719443     1.01   0.315    -.4508988    1.399815
     21559 1  |   .4364035   .4743872     0.92   0.358    -.4937433     1.36655
     21560 1  |   .4145833   .4737109     0.88   0.382    -.5142374    1.343404
     21561 1  |   .2991228   .4844826     0.62   0.537    -.6508183    1.249064
     21562 1  |        .75   .4855914     1.54   0.123    -.2021151    1.702115
     21563 1  |   .6131714   .4710288     1.30   0.193    -.3103905    1.536733
     21564 1  |   .4635135   .4728693     0.98   0.327    -.4636571    1.390684
     21565 1  |   .4662162   .4716098     0.99   0.323    -.4584847    1.390917
     21566 1  |   .5331766   .4737512     1.13   0.260    -.3957231    1.462076
     21567 1  |   .6704545   .4751793     1.41   0.158    -.2612453    1.602154
     21568 1  |   .5257353   .4787593     1.10   0.272     -.412984    1.464455
     21569 1  |   .6166667     .48397     1.27   0.203    -.3322694    1.565603
     21570 1  |   .6388889    .473019     1.35   0.177    -.2885753    1.566353
     21571 1  |   .5082583   .4717599     1.08   0.281    -.4167371    1.433254
     21572 1  |   .5169643   .4727618     1.09   0.274    -.4099955    1.443924
     21573 1  |   .6126374   .4732503     1.29   0.196    -.3152802    1.540555
     21574 1  |   .2998891   .4747573     0.63   0.528    -.6309834    1.230762
     21575 1  |   .4459064   .4823146     0.92   0.355    -.4997839    1.391597
     21576 1  |   .5448718   .4923918     1.11   0.269    -.4205772    1.510321
     21577 1  |   .4695946   .4745286     0.99   0.322    -.4608294    1.400019
     21578 1  |   .3676471   .4725627     0.78   0.437    -.5589223    1.294216
     21579 1  |   .4605263   .4716177     0.98   0.329    -.4641901    1.385243
     21580 1  |   .3259331   .4757102     0.69   0.493    -.6068078    1.258674
     21581 1  |       1.25   .7677873     1.63   0.104    -.2554262    2.755426
              |
    1.neutral |  -.0467775   .0934132    -0.50   0.617     -.229936    .1363809
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |  -.1865558   .2155316    -0.87   0.387    -.6091557    .2360442
     21552 1  |  -.1832365   .1413163    -1.30   0.195    -.4603201    .0938471
     21553 1  |   .0089791   .1373263     0.07   0.948    -.2602811    .2782393
     21554 1  |  -.1950525   .1637348    -1.19   0.234    -.5160928    .1259878
     21555 1  |  -.0018054   .1821129    -0.01   0.992    -.3588803    .3552694
     21556 1  |   .0134442   .1332854     0.10   0.920    -.2478929    .2747813
     21557 1  |  -.0582008   .1334576    -0.44   0.663    -.3198755    .2034739
     21558 1  |   .0351911   .1308908     0.27   0.788    -.2214509    .2918331
     21559 1  |   .0248477   .1297467     0.19   0.848     -.229551    .2792464
     21560 1  |   -.242966   .1365345    -1.78   0.075    -.5106739    .0247418
     21561 1  |    -.12898   .1728482    -0.75   0.456    -.4678892    .2099291
     21562 1  |  -.0102877   .1694629    -0.06   0.952    -.3425593    .3219839
     21563 1  |  -.0212646   .1307278    -0.16   0.871    -.2775869    .2350577
     21564 1  |   .0850128   .1333721     0.64   0.524    -.1764942    .3465199
     21565 1  |   .0557866    .128679     0.43   0.665    -.1965187    .3080918
     21566 1  |   .0770806   .1339242     0.58   0.565     -.185509    .3396702
     21567 1  |   .1810368   .1417015     1.28   0.201     -.096802    .4588757
     21568 1  |   .1766795   .1663803     1.06   0.288     -.149548     .502907
     21569 1  |   .0967775   .1661022     0.58   0.560    -.2289046    .4224597
     21570 1  |   .0745553   .1287866     0.58   0.563    -.1779609    .3270716
     21571 1  |  -.0219483   .1285272    -0.17   0.864    -.2739558    .2300592
     21572 1  |    .037705   .1329044     0.28   0.777    -.2228851     .298295
     21573 1  |  -.0223469   .1275147    -0.18   0.861    -.2723693    .2276755
     21574 1  |   .0726025   .1304829     0.56   0.578    -.1832397    .3284447
     21575 1  |  -.1198891   .1618413    -0.74   0.459    -.4372168    .1974386
     21576 1  |   .1742285   .1885269     0.92   0.355    -.1954224    .5438794
     21577 1  |  -.1141834   .1342251    -0.85   0.395     -.377363    .1489961
     21578 1  |    .007912   .1328262     0.06   0.953    -.2525247    .2683487
     21579 1  |   -.091181   .1286405    -0.71   0.478    -.3434108    .1610488
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |        .25   .1535575     1.63   0.104    -.0510852    .5510852
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatter coefL event_days
>  if timeWindow3_ada & !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL event_days if timeWin
> dow3_ada & neutral & n_DOP==1 & coefL>.2, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "Pro-Opp TV"
>  2 "Pro-Gov TV" 3 "Neutral TV")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)) xtitl
> e("Days since event") ytitle("Threat mentions in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(solid) lc(
> gray))) (lpoly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) msize(small
> )) (lpoly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize(small))  (l
> poly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msiz
> e(small)) (lpoly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) msymbol(S) color
> (navy) msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3) msymbol(S) col
> or(green) msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & post_ada, bwidth(3) msymbol(S) c
> olor(green) msize(small)), name(g1, replace) nodraw
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA16a.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA16a.png written in PNG format)

. drop coefL

. 
. 
. **criticism**
. set matsize 5000

. reg criticism i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =      1.45
       Model |  12.0589486        95  .126936301   Prob > F        =    0.0033
    Residual |  270.142875     3,085  .087566572   R-squared       =    0.0427
-------------+----------------------------------   Adj R-squared   =    0.0133
       Total |  282.201823     3,180  .088742712   Root MSE        =    .29592

-------------------------------------------------------------------------------
    criticism |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |      -.125   .1295515    -0.96   0.335    -.3790159    .1290159
       21551  |      -.025   .1177485    -0.21   0.832    -.2558734    .2058734
       21552  |   .0054348   .1214622     0.04   0.964    -.2327202    .2435897
       21553  |  -.0215517   .1181751    -0.18   0.855    -.2532616    .2101582
       21554  |      -.125   .1268732    -0.99   0.325    -.3737644    .1237644
       21555  |      -.125   .1329726    -0.94   0.347    -.3857238    .1357238
       21556  |     -.0625   .1169713    -0.53   0.593    -.2918494    .1668494
       21557  |  -.0643939   .1166163    -0.55   0.581    -.2930473    .1642595
       21558  |  -.0367647   .1162812    -0.32   0.752     -.264761    .1912316
       21559  |  -.0723684   .1151095    -0.63   0.530    -.2980675    .1533307
       21560  |  -.0916667   .1177485    -0.78   0.436      -.32254    .1392067
       21561  |      -.125   .1295515    -0.96   0.335    -.3790159    .1290159
       21562  |   3.07e-14   .1281356     0.00   1.000    -.2512397    .2512397
       21563  |   .0514706   .1162812     0.44   0.658    -.1765258    .2794669
       21564  |  -.0916667   .1177485    -0.78   0.436      -.32254    .1392067
       21565  |  -.0439189   .1153797    -0.38   0.703    -.2701478      .18231
       21566  |  -.0643939   .1166163    -0.55   0.581    -.2930473    .1642595
       21567  |      -.125   .1208074    -1.03   0.301    -.3618711    .1118711
       21568  |  -.0073529   .1268732    -0.06   0.954    -.2561173    .2414115
       21569  |      -.075   .1237908    -0.61   0.545    -.3177206    .1677206
       21570  |   .0416667   .1156643     0.36   0.719    -.1851201    .2684535
       21571  |  -.0709459   .1153797    -0.61   0.539    -.2971748     .155283
       21572  |    -.03125   .1169713    -0.27   0.789    -.2605994    .1980994
       21573  |   -.077381   .1141523    -0.68   0.498    -.3012031    .1464412
       21574  |  -.0762195   .1143748    -0.67   0.505     -.300478    .1480389
       21575  |  -.0694444   .1257403    -0.55   0.581    -.3159877    .1770988
       21576  |   .0416667   .1350668     0.31   0.758    -.2231633    .3064966
       21577  |   .0641892   .1153797     0.56   0.578    -.1620397    .2904181
       21578  |   .0514706   .1162812     0.44   0.658    -.1765258    .2794669
       21579  |  -.0986842   .1151095    -0.86   0.391    -.3243833    .1270149
       21580  |   .0101351   .1153797     0.09   0.930    -.2160938     .236364
       21581  |      -.125   .3138668    -0.40   0.690    -.7404092    .4904092
              |
     1.proopp |      -.125   .3138668    -0.40   0.690    -.7404092    .4904092
              |
 date2#proopp |
     21550 1  |       .125   .3395526     0.37   0.713    -.5407721    .7907721
     21551 1  |   .0634615   .3237271     0.20   0.845    -.5712809     .698204
     21552 1  |   .0686393   .3249044     0.21   0.833    -.5684115    .7056901
     21553 1  |    .150584   .3230428     0.47   0.641    -.4828168    .7839848
     21554 1  |   .2361111   .3294362     0.72   0.474    -.4098253    .8820475
     21555 1  |   .1805556   .3318329     0.54   0.586    -.4700803    .8311914
     21556 1  |   .1534091   .3223389     0.48   0.634    -.4786116    .7854297
     21557 1  |   .1977273   .3226218     0.61   0.540     -.434848    .8303025
     21558 1  |   .0893963   .3215467     0.28   0.781     -.541071    .7198635
     21559 1  |   .1557018   .3232111     0.48   0.630    -.4780289    .7894324
     21560 1  |   .1229167   .3227503     0.38   0.703    -.5099105    .7557438
     21561 1  |   .1776316   .3300893     0.54   0.591    -.4695854    .8248486
     21562 1  |      .1875   .3308447     0.57   0.571    -.4611982    .8361982
     21563 1  |   .1224425   .3209229     0.38   0.703    -.5068017    .7516866
     21564 1  |   .2538288   .3221769     0.79   0.431    -.3778741    .8855318
     21565 1  |   .2060811   .3213187     0.64   0.521    -.4239392    .8361014
     21566 1  |   .2712905   .3227777     0.84   0.401    -.3615905    .9041715
     21567 1  |   .2159091   .3237507     0.67   0.505    -.4188797    .8506979
     21568 1  |   .0698529   .3261899     0.21   0.830    -.5697184    .7094242
     21569 1  |       .275     .32974     0.83   0.404    -.3715323    .9215323
     21570 1  |       .125   .3222789     0.39   0.698    -.5069029    .7569029
     21571 1  |   .2653904    .321421     0.83   0.409    -.3648305    .8956112
     21572 1  |   .1455357   .3221036     0.45   0.651    -.4860236     .777095
     21573 1  |   .3850733   .3224364     1.19   0.232    -.2471386    1.017285
     21574 1  |   .1216741   .3234632     0.38   0.707    -.5125511    .7558992
     21575 1  |   .2799708   .3286122     0.85   0.394    -.3643501    .9242916
     21576 1  |  -.0416667    .335478    -0.12   0.901    -.6994496    .6161162
     21577 1  |   .1024775   .3233074     0.32   0.751    -.5314421     .736397
     21578 1  |   .2132353    .321968     0.66   0.508     -.418058    .8445286
     21579 1  |   .2653509   .3213241     0.83   0.409      -.36468    .8953817
     21580 1  |   .1803411   .3241125     0.56   0.578     -.455157    .8158392
     21581 1  |       .125   .5231114     0.24   0.811    -.9006819    1.150682
              |
    1.neutral |  -.0005198   .0636446    -0.01   0.993    -.1253099    .1242704
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |  -.0994802   .1468467    -0.68   0.498    -.3874074    .1884469
     21552 1  |  -.0331408   .0962821    -0.34   0.731    -.2219244    .1556427
     21553 1  |  -.0260054   .0935636    -0.28   0.781    -.2094587    .1574478
     21554 1  |   .0745938   .1115563     0.67   0.504    -.1441384    .2933261
     21555 1  |   .0531513   .1240778     0.43   0.668    -.1901321    .2964347
     21556 1  |   .0046864   .0908104     0.05   0.959    -.1733686    .1827415
     21557 1  |  -.0065149   .0909278    -0.07   0.943       -.1848    .1717702
     21558 1  |   .0183451    .089179     0.21   0.837    -.1565111    .1932013
     21559 1  |   .0312215   .0883995     0.35   0.724    -.1421062    .2045492
     21560 1  |   .0441095   .0930242     0.47   0.635    -.1382861    .2265051
     21561 1  |   .0459743   .1177655     0.39   0.696    -.1849324     .276881
     21562 1  |   -.081002    .115459    -0.70   0.483    -.3073864    .1453824
     21563 1  |   -.101324   .0890679    -1.14   0.255    -.2759623    .0733144
     21564 1  |  -.0181077   .0908695    -0.20   0.842    -.1962786    .1600632
     21565 1  |  -.0225903    .087672    -0.26   0.797    -.1944917    .1493111
     21566 1  |  -.0045308   .0912457    -0.05   0.960    -.1834392    .1743777
     21567 1  |   .0190383   .0965445     0.20   0.844    -.1702598    .2083363
     21568 1  |   -.033794   .1133588    -0.30   0.766    -.2560604    .1884724
     21569 1  |   .0505198   .1131693     0.45   0.655    -.1713751    .2724146
     21570 1  |  -.0828136   .0877453    -0.94   0.345    -.2548588    .0892316
     21571 1  |     .03218   .0875686     0.37   0.713    -.1395186    .2038786
     21572 1  |   .0680601   .0905508     0.75   0.452     -.109486    .2456061
     21573 1  |   .0335459   .0868788     0.39   0.699    -.1368002    .2038919
     21574 1  |   .0301706   .0889011     0.34   0.734    -.1441406    .2044819
     21575 1  |   .0560753   .1102663     0.51   0.611    -.1601275    .2722781
     21576 1  |  -.1661469   .1284478    -1.29   0.196    -.4179987    .0857049
     21577 1  |  -.0331139   .0914507    -0.36   0.717    -.2124242    .1461965
     21578 1  |   .0919063   .0904976     1.02   0.310    -.0855353    .2693479
     21579 1  |   .0954161   .0876458     1.09   0.276     -.076434    .2672661
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |       .125   .1046223     1.19   0.232    -.0801364    .3301364
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatter coefL event_days
>  if timeWindow3_ada & !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL event_days if timeWin
> dow3_ada & neutral & n_DOP==1, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "Pro-Opp TV" 2 "Pro-Gov
>  TV" 3 "Neutral TV")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)) xtitle("Days sin
> ce event") ytitle("Critical remarks in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(solid) lc(gray))) (l
> poly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) msize(small)) (lpoly 
> coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize(small))  (lpoly coefL
>  event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msize(small)) 
> (lpoly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msi
> ze(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3) msymbol(S) color(green) 
> msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & post_ada, bwidth(3) msymbol(S) color(green
> ) msize(small)), name(g1, replace) nodraw 
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA16b.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA16b.png written in PNG format)

. drop coefL

. 
. 
. 
. **** Figure A17 ****
. 
. set matsize 5000

. reg twitter i.date2##proopp  i.date2##neutral
note: 21549b.date2#1.neutral identifies no observations in the sample
note: 21550.date2#1.neutral identifies no observations in the sample
note: 21580.date2#1.neutral omitted because of collinearity
note: 21581.date2#1.neutral identifies no observations in the sample

      Source |       SS           df       MS      Number of obs   =     3,181
-------------+----------------------------------   F(95, 3085)     =      1.44
       Model |  15.8324958        95  .166657851   Prob > F        =    0.0037
    Residual |  356.770459     3,085  .115646826   R-squared       =    0.0425
-------------+----------------------------------   Adj R-squared   =    0.0130
       Total |  372.602955     3,180  .117170741   Root MSE        =    .34007

-------------------------------------------------------------------------------
      twitter |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
        date2 |
       21550  |   .1333333   .1488813     0.90   0.371    -.1585832    .4252499
       21551  |   .1333333   .1353172     0.99   0.325    -.1319876    .3986543
       21552  |   .1304348    .139585     0.93   0.350    -.1432542    .4041238
       21553  |    .137931   .1358075     1.02   0.310    -.1283512    .4042133
       21554  |   .4117647   .1458033     2.82   0.005     .1258833    .6976461
       21555  |   2.37e-14   .1528128     0.00   1.000    -.2996252    .2996252
       21556  |     .09375    .134424     0.70   0.486    -.1698196    .3573196
       21557  |   .0606061   .1340161     0.45   0.651    -.2021637    .3233758
       21558  |   .1176471   .1336309     0.88   0.379    -.1443676    .3796617
       21559  |   .1052632   .1322845     0.80   0.426    -.1541115    .3646378
       21560  |         .2   .1353172     1.48   0.140     -.065321     .465321
       21561  |   .3333333   .1488813     2.24   0.025     .0414168    .6252499
       21562  |      .1875   .1472541     1.27   0.203    -.1012261    .4762261
       21563  |   .2058824   .1336309     1.54   0.123    -.0561323     .467897
       21564  |   .0666667   .1353172     0.49   0.622    -.1986543    .3319876
       21565  |   .0810811    .132595     0.61   0.541    -.1789024    .3410646
       21566  |   .1515152   .1340161     1.13   0.258    -.1112546    .4142849
       21567  |       .125   .1388325     0.90   0.368    -.1472136    .3972136
       21568  |   .1176471   .1458033     0.81   0.420    -.1682344    .4035285
       21569  |        .25    .142261     1.76   0.079    -.0289359    .5289359
       21570  |        .25    .132922     1.88   0.060    -.0106246    .5106246
       21571  |   .1351351    .132595     1.02   0.308    -.1248484    .3951186
       21572  |     .21875    .134424     1.63   0.104    -.0448196    .4823196
       21573  |   .1428571   .1311844     1.09   0.276    -.1143605    .4000748
       21574  |   .0487805   .1314401     0.37   0.711    -.2089385    .3064995
       21575  |   .2222222   .1445015     1.54   0.124    -.0611066    .5055511
       21576  |   .1666667   .1552195     1.07   0.283    -.1376773    .4710107
       21577  |   .2432432    .132595     1.83   0.067    -.0167402    .5032267
       21578  |   .1176471   .1336309     0.88   0.379    -.1443676    .3796617
       21579  |   .1315789   .1322845     0.99   0.320    -.1277957    .3909536
       21580  |   .1351351    .132595     1.02   0.308    -.1248484    .3951186
       21581  |   2.32e-14   .3606975     0.00   1.000    -.7072316    .7072316
              |
     1.proopp |          1   .3606975     2.77   0.006     .2927684    1.707232
              |
 date2#proopp |
     21550 1  |  -1.008333   .3902157    -2.58   0.010    -1.773442   -.2432244
     21551 1  |  -1.017949   .3720289    -2.74   0.006    -1.747398   -.2884992
     21552 1  |  -1.056361   .3733819    -2.83   0.005    -1.788463   -.3242585
     21553 1  |  -.9766407   .3712426    -2.63   0.009    -1.704548   -.2487331
     21554 1  |  -1.189542   .3785898    -3.14   0.002    -1.931856   -.4472288
     21555 1  |  -.8333333   .3813442    -2.19   0.029    -1.581048    -.085619
     21556 1  |  -1.063447   .3704337    -2.87   0.004    -1.789769   -.3371254
     21557 1  |  -.9272727   .3707587    -2.50   0.012    -1.654232   -.2003138
     21558 1  |  -1.012384   .3695232    -2.74   0.006     -1.73692   -.2878475
     21559 1  |   -1.02193   .3714359    -2.75   0.006    -1.750217   -.2936431
     21560 1  |   -1.10625   .3709064    -2.98   0.003    -1.833498   -.3790015
     21561 1  |  -1.175439   .3793404    -3.10   0.002    -1.919224   -.4316533
     21562 1  |     -.9375   .3802085    -2.47   0.014    -1.682988   -.1920125
     21563 1  |   -.988491   .3688063    -2.68   0.007    -1.711622   -.2653602
     21564 1  |  -.8504505   .3702474    -2.30   0.022    -1.576407    -.124494
     21565 1  |  -.8648649   .3692612    -2.34   0.019    -1.588888   -.1408421
     21566 1  |  -1.048067   .3709379    -2.83   0.005    -1.775377   -.3207565
     21567 1  |  -1.034091   .3720561    -2.78   0.005    -1.763594   -.3045881
     21568 1  |  -1.086397   .3748592    -2.90   0.004    -1.821396   -.3513982
     21569 1  |  -1.183333   .3789391    -3.12   0.002    -1.926332   -.4403349
     21570 1  |  -1.016667   .3703647    -2.75   0.006    -1.742853   -.2904804
     21571 1  |  -.8295796   .3693788    -2.25   0.025    -1.553833   -.1053263
     21572 1  |  -1.104464   .3701633    -2.98   0.003    -1.830256   -.3786729
     21573 1  |  -1.104396   .3705457    -2.98   0.003    -1.830937   -.3778543
     21574 1  |  -1.003326   .3717257    -2.70   0.007    -1.732181    -.274471
     21575 1  |  -1.116959   .3776429    -2.96   0.003    -1.857416    -.376502
     21576 1  |  -1.166667   .3855332    -3.03   0.002    -1.922594    -.410739
     21577 1  |  -.9515766   .3715466    -2.56   0.010     -1.68008   -.2230728
     21578 1  |  -.9411765   .3700073    -2.54   0.011    -1.666662   -.2156908
     21579 1  |  -.9649123   .3692674    -2.61   0.009    -1.688947   -.2408774
     21580 1  |  -1.087516   .3724718    -2.92   0.004    -1.817834   -.3571982
     21581 1  |         -1   .6011625    -1.66   0.096    -2.178719    .1787193
              |
    1.neutral |  -.0197505   .0731407    -0.27   0.787      -.16316     .123659
              |
date2#neutral |
     21549 1  |          0  (empty)
     21550 1  |          0  (empty)
     21551 1  |   .0530839    .168757     0.31   0.753    -.2778037    .3839714
     21552 1  |    .018348   .1106479     0.17   0.868    -.1986031     .235299
     21553 1  |   .0164349   .1075238     0.15   0.879    -.1943906    .2272603
     21554 1  |   -.169792   .1282012    -1.32   0.185    -.4211602    .0815763
     21555 1  |   .1250137   .1425909     0.88   0.381     -.154569    .4045963
     21556 1  |  -.0573328   .1043598    -0.55   0.583    -.2619546     .147289
     21557 1  |    .155573   .1044947     1.49   0.137    -.0493132    .3604592
     21558 1  |   .0384671    .102485     0.38   0.707    -.1624786    .2394128
     21559 1  |  -.0021793   .1015891    -0.02   0.983    -.2013685    .1970099
     21560 1  |  -.0840956   .1069039    -0.79   0.432    -.2937056    .1255144
     21561 1  |  -.1317646   .1353367    -0.97   0.330    -.3971239    .1335946
     21562 1  |   .0931201   .1326862     0.70   0.483    -.1670421    .3532822
     21563 1  |   .0377488   .1023573     0.37   0.712    -.1629466    .2384441
     21564 1  |   .0854368   .1044277     0.82   0.413    -.1193181    .2901917
     21565 1  |   .0546115   .1007532     0.54   0.588    -.1429386    .2521615
     21566 1  |   .0719391     .10486     0.69   0.493    -.1336635    .2775416
     21567 1  |   -.086731   .1109495    -0.78   0.434    -.3042733    .1308114
     21568 1  |  -.0562299   .1302726    -0.43   0.666    -.3116596    .1991999
     21569 1  |  -.0802495   .1300548    -0.62   0.537    -.3352522    .1747533
     21570 1  |  -.1330273   .1008374    -1.32   0.187    -.3307425     .064688
     21571 1  |  -.0010989   .1006343    -0.01   0.991    -.1984158     .196218
     21572 1  |  -.1183543   .1040615    -1.14   0.255    -.3223912    .0856826
     21573 1  |  -.0263324   .0998415    -0.26   0.792    -.2220951    .1694302
     21574 1  |   .0297936   .1021656     0.29   0.771    -.1705259     .230113
     21575 1  |  -.1283976   .1267186    -1.01   0.311     -.376859    .1200638
     21576 1  |    .088378   .1476129     0.60   0.549    -.2010515    .3778074
     21577 1  |  -.0679372   .1050956    -0.65   0.518    -.2740016    .1381273
     21578 1  |   .0806749   .1040003     0.78   0.438     -.123242    .2845918
     21579 1  |   .0093837    .100723     0.09   0.926    -.1881073    .2068747
     21580 1  |          0  (omitted)
     21581 1  |          0  (empty)
              |
        _cons |  -2.34e-14   .1202325    -0.00   1.000    -.2357439    .2357439
-------------------------------------------------------------------------------

. predict coefL
(option xb assumed; fitted values)

. 
. twoway (scatter coefL event_days if timeWindow3_ada & proopp & n_DOP==1, color(orange*0.62) msize(vsmall)) (scatter coefL event_days
>  if timeWindow3_ada & !neutral & !proopp & n_DOP==1, msymbol(S) color(navy*0.62) msize(vsmall)) (scatter coefL event_days if timeWin
> dow3_ada & neutral & n_DOP==1, scale(0.9) msymbol(T) color(green*0.62) msize(vsmall) legend( cols(1) order(1 "Pro-Opp TV" 2 "Pro-Gov
>  TV" 3 "Neutral TV")) xsize(8) graphregion(fcolor(white) lcolor(white) lwidth(thin) ifcolor (white) ilcolor(white)) xtitle("Days sin
> ce event") ytitle("Twitter mentions in news media (%)") aspectratio(0.5) graphregion(lwidth(small)) tline(0, lp(solid) lc(gray))) (l
> poly coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & !post_ada, bwidth(3) lp(longdash) color(orange) msize(small)) (lpoly 
> coefL event_days if timeWindow3_ada & proopp & n_DOP==1 & post_ada, bwidth(3) lp(longdash) color(orange) msize(small))  (lpoly coefL
>  event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & !post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msize(small)) 
> (lpoly coefL event_days if timeWindow3_ada & !neutral & !proopp & n_DOP==1 & post_ada, bwidth(3) lp(dash) msymbol(S) color(navy) msi
> ze(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & !post_ada, bwidth(3) msymbol(S) color(green) 
> msize(small)) (lpoly coefL event_days if timeWindow3_ada & !progov & !proopp & n_DOP==1 & post_ada, bwidth(3) msymbol(S) color(green
> ) msize(small)), name(g1, replace) nodraw 
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph combine g1, col(1) scale(1.1) graphregion(color(white))
(note:  named style small not found in class linewidth, default attributes used)
(note:  linewidth not found in scheme, default attributes used)

. graph export "$PathFig/FigureA17.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures//FigureA17.png written in PNG format)

. drop coefL

. 
. 
. 
end of do-file

. do "6_twitter_manualcoding.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All tables and graphs relying on manual coding of tweets 
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. * Path 
. 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales" {
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}sept2021export.dta", clear

. 
. 
. 
. drop mentionsOther opMentions govMentions mentionOther post_opo_ada post_mention_ada post_opo_mention_ada opo_mention mention_ada me
> ntion_opo_ada mention_kud mention_opo_kud

. 
. replace text = " " + text
(3,979 real changes made)

. gen opMentions = strpos(text," rzd") + strpos(text," min ") + strpos(text,"minist") + strpos(text,"wicemin") + strpos(text,"pis ") +
>  strpos(text,"tvp") + strpos(text,"andruszkiewicz") + strpos(text,"berni_krynick") + strpos(text,"budet") + strpos(text,"premier") +
>  strpos(text,"morawieck") + strpos(text,"pisowsk") + strpos(text,"macierewicz") + strpos(text,"kaczysk") + strpos(text," msz ") + st
> rpos(text,"tygodnik_sieci") + strpos(text,"smolesk") + strpos(text,"misiewicz") + strpos(text,"tasmykaczynsk") + strpos(text,"glapis
> ki") + strpos(text,"glapinski") + strpos(text,"szydo") + strpos(text,"ziobro") + strpos(text,"zieliski") + strpos(text,"gowin") + st
> rpos(text,"jbrudzinski") + strpos(text,"drelich") + strpos(text,"terlecki") + strpos(text,"sdownictw") + strpos(text,"pisorgpl") + s
> trpos(text," nbp ") + strpos(text,"wicemarsz") + strpos(text," cba ") + strpos(text,"patryk jaki") + strpos(text,"policj") + strpos(
> text,"czaputowicz") + strpos(text,"gliski")

. replace opMentions = opMentions>0
(913 real changes made)

. gen govMentions = strpos(text," opozycj") + strpos(text,"bzdrojewski") + strpos(text," lewic") + strpos(text,"olejnik") + strpos(tex
> t," lewicow") + strpos(text," biedro") + strpos(text," lewac") + strpos(text," psl_") + strpos(text," psl ") + strpos(text,"gazwyb")
>  + strpos(text," nowick") + strpos(text," sikorsk") + strpos(text,"schetyn") + strpos(text," neuman") + strpos(text,"tomasz_lis") + 
> strpos(text,"tomasz lis") + strpos(text," lis_tomasz") + strpos(text," tusk") + strpos(text," platform") + strpos(text,"trzaskowski_
> ") + strpos(text,"platforma_org") + strpos(text,"klubnauer") + strpos(text," gw_") + strpos(text,"gazetawyborcza") + strpos(text,"tv
> n24")

. replace govMentions = govMentions>0
(345 real changes made)

. replace govMentions = govMentions + strpos(text,"platforma_org")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(14 real changes made)

. replace govMentions = govMentions + strpos(text,"arlukowicz")>0
(6 real changes made)

. replace govMentions = govMentions + strpos(text,"bbudka")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"andrzejhalicki")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"slawekneumann")>0
(11 real changes made)

. replace govMentions = govMentions + strpos(text,"schetynadlapo")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mkierwinski")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"jangrabiec")>0
(2 real changes made)

. replace govMentions = govMentions + strpos(text,"achybicka")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"protasiewiczj")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"zpawlowicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"trzaskowski_")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszlenz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"hannagw")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"asia_mucha")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"niesiolowskis")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"bbukiewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"c_grabarczyk")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"ikatarasinska")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"ctomczyk")>0
(10 real changes made)

. replace govMentions = govMentions + strpos(text,"sowamarek")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"ireneuszras")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"kopacz_ewa")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"m_k_blonska")>0
(3 real changes made)

. replace govMentions = govMentions + strpos(text,"adam_korol")>0
(7 real changes made)

. replace govMentions = govMentions + strpos(text,"pomaska")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"henryka_henia50")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"miroslawanykiel")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"marianzembala")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"urszulaaugustyn")>0
(2 real changes made)

. replace govMentions = govMentions + strpos(text,"kr_szumilas")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"okladrewnowicz")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"mswitczak")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"jakubrutnicki")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mjanyska")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"ziolkowskiszym")>0
(16 real changes made)

. replace govMentions = govMentions + strpos(text,"waldydzikowski")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"slawomirnitras")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"krzysztofbrejza")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"arkadiuszmyrcha")>0
(6 real changes made)

. replace govMentions = govMentions + strpos(text,"mwielichowska")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"gajewska_kinga")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"newsplatforma")>0
(6 real changes made)

. replace govMentions = govMentions + strpos(text,"grzegorzfurgo")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"bborusewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"rtyszkiewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"jaroslawduda")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"zdzislaw_gawlik")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"arturgierada")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"a_marchewka")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"jacek_protas")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"wojciechsaluga")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"wslugocki")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"hannazdanowska")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"prezydentzuk")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"izabela_debska")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tadeuszzwiefka")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"donaldtusk")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"piotr_naimski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"pisorgpl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"morawieckim")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jbrudzinski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"patrykjaki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"andzyberto")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"kurskipl")>0
(5 real changes made)

. replace opMentions = opMentions + strpos(text,"piotrglinski")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"jaroslaw_gowin")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"stkarczewski")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"marekkuchcinski")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"beataszydlo")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"beatamk")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"e_rafalska")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"krystpawlowicz")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"ziobropl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"beatakempa_kprm")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mblaszczak")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"macierewicz_a")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"elzbietawitek")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"_annazalewska")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"michaldworczyk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"latostomasz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"slawzawislak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"wassermann_ma")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"arekmularczyk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"w_bernacki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mareksuski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"akosztowniak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"minenergii")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"a_czartoryski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mariuszkaminsk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"sasinjacek ?")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"bogdan_rzonca")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejszlachta")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mrirw_gov_pl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"d_piontkowski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mkidn_gov_pl")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"stanislaw_szwed")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szymongizynski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"izabelakloc")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejduda")>0
(11 real changes made)

. replace opMentions = opMentions + strpos(text,"annamkrupka")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"iwonaarent")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"joannalichocka")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldczarneck3")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"tadeuszdziuba")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"zagorskimarek")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"marekgrobarczyk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"amadamczyk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jerzykwiecinski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jemilewicz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldbanka")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jczaputowicz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szumowskilukasz")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"j_kopcinska")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"profkarski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"ryszardterlecki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"r_czarnecki")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"grzegorzczelej")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"anita_cz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"ministerjurgiel")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"cymanskitadeusz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jacekzalek")>0
(0 real changes made)

. gen mentionOther = (opMentions & opposition) | (govMentions & government)

. replace mentionOther = 0 if mentionOther == .
(0 real changes made)

. 
. gen post_opo_ada = post_ada*opposition

. gen post_mention_ada = post_ada*mentionOther

. gen post_opo_mention_ada = post_ada*opposition*mentionOther

. gen opo_mention = opposition*mentionOther

. 
. gen mention_ada = mentionOther*gg_adamowicz

. gen mention_opo_ada= mentionOther*gg_adamowicz*opposition

. 
. 
. gen post_mp_ada = post_ada*mp

. gen post_opo_mp = post_opo_ada*mp

. 
. 
. **** negative campaigning manually coded ****
. 
. replace humor = 0 if humor==.
(3,659 real changes made)

. replace threat = 0 if threat==.
(3,696 real changes made)

. replace criticism = 0 if criticism==.
(3,641 real changes made)

. 
. label variable humor "Humorous criticism"

. label variable criticism "Explicit criticism"

. label variable threat "Threat"

. 
. gen post_humor = post_ada * humor

. gen post_threat = post_ada * threat

. gen post_criticism = post_ada * criticism

. 
. label variable post_humor "Post x Humorous criticism"

. label variable post_criticism "Post x Explicit criticism"

. label variable post_threat "Post x Threat"

. 
. label variable post_opo_ada "Post x Opposition"

. 
. 
. label variable opposition "Party"

. label define select 0 "Government" 1 "Opposition" 

. label values opposition select

. 
. label variable post_ada "Attack"

. label define select2 0 "Pre" 1 "Post" 

. label values post_ada select2

. 
. 
. 
. 
. *** Figure A8 ***
. 
. 
. cibar criticism if !gg_adamowicz & mp, over1(post_ada) over2(opposition)

. cibar threat if !gg_adamowicz & mp, over1(post_ada) over2(opposition)

. cibar humor if !gg_adamowicz & mp, over1(post_ada) over2(opposition)

. cibar mentionOther if !gg_adamowicz & mp, over1(post_ada) over2(opposition)

. 
. 
. 
. 
.  **** Table A9  ****
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement post_opo_ada humor post_humor criticism post_criticism threat post_threat hour_s
> q hour gg_hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(  13,     85) =      22.81
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7499
                                                  Adj R-squared   =     0.7416
                                                  Within R-sq.    =     0.4330
Number of clusters (userid)  =         86         Root MSE        =     1.1188

                                  (Std. Err. adjusted for 86 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  post_opo_ada |  -.2277832    .188257    -1.21   0.230    -.6020886    .1465223
         humor |   .7309907   .1428689     5.12   0.000      .446929    1.015052
    post_humor |  -.2916792   .2559476    -1.14   0.258    -.8005716    .2172132
     criticism |     .82128   .1307967     6.28   0.000     .5612211    1.081339
post_criticism |  -.4946113   .2302132    -2.15   0.035    -.9523369   -.0368858
        threat |   1.072772    .163545     6.56   0.000     .7476011    1.397944
   post_threat |  -.6150118   .2583775    -2.38   0.020    -1.128736    -.101288
       hour_sq |  -.0011268   .0005676    -1.99   0.050    -.0022554    1.80e-06
          hour |   .0249953   .0151649     1.65   0.103    -.0051566    .0551471
    gg_hashtag |  -.1724225   .1249427    -1.38   0.171     -.420842    .0759971
         gg_at |  -.4155519   .1080076    -3.85   0.000    -.6302999    -.200804
      gg_reply |  -1.874008   .3139481    -5.97   0.000    -2.498221   -1.249795
         gg_rt |          0  (omitted)
       gg_http |   .3272225   .0700205     4.67   0.000      .188003    .4664421
         _cons |   3.412364   .1744111    19.57   0.000     3.065588     3.75914
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt post_opo_ada humor post_humor criticism post_criticism threat post_threat hour_sq hour gg_
> hashtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(  13,     85) =      24.45
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7412
                                                  Adj R-squared   =     0.7326
                                                  Within R-sq.    =     0.4664
Number of clusters (userid)  =         86         Root MSE        =     0.9112

                                  (Std. Err. adjusted for 86 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
        log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  post_opo_ada |  -.1617193   .1142313    -1.42   0.161    -.3888417    .0654031
         humor |    .636714    .112668     5.65   0.000     .4126999    .8607281
    post_humor |  -.3504302   .2026874    -1.73   0.087     -.753427    .0525666
     criticism |   .7724239   .1075227     7.18   0.000       .55864    .9862079
post_criticism |  -.5370057   .1834946    -2.93   0.004    -.9018422   -.1721692
        threat |   1.001114   .1713214     5.84   0.000     .6604813    1.341747
   post_threat |   -.653791   .2760793    -2.37   0.020    -1.202711   -.1048714
       hour_sq |  -.0011678    .000472    -2.47   0.015    -.0021062   -.0002294
          hour |   .0216049   .0128426     1.68   0.096    -.0039295    .0471394
    gg_hashtag |  -.0942105   .1207234    -0.78   0.437     -.334241      .14582
         gg_at |  -.3039729   .0958935    -3.17   0.002     -.494635   -.1133109
      gg_reply |   -1.65609    .262398    -6.31   0.000    -2.177807   -1.134372
         gg_rt |          0  (omitted)
       gg_http |   .3029955   .0528142     5.74   0.000     .1979868    .4080042
         _cons |   2.136182   .1446398    14.77   0.000       1.8486    2.423765
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav post_opo_ada humor post_humor criticism post_criticism threat post_threat hour_sq hour gg_ha
> shtag gg_at gg_reply gg_rt gg_http if timeWindow3_ada & !gg_rt, cl(userid) abs(userid date2)
(dropped 2 singleton observations)
(MWFE estimator converged in 9 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =      3,977
Absorbing 2 HDFE groups                           F(  13,     85) =      20.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7440
                                                  Adj R-squared   =     0.7355
                                                  Within R-sq.    =     0.4103
Number of clusters (userid)  =         86         Root MSE        =     1.0995

                                  (Std. Err. adjusted for 86 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
       log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  post_opo_ada |  -.2226258    .191578    -1.16   0.248    -.6035342    .1582825
         humor |   .7157714   .1457274     4.91   0.000     .4260264    1.005516
    post_humor |  -.2779365   .2472101    -1.12   0.264    -.7694563    .2135833
     criticism |   .7920835   .1277749     6.20   0.000     .5380327    1.046134
post_criticism |  -.4585453   .2205566    -2.08   0.041    -.8970709   -.0200197
        threat |   1.031861   .1588811     6.49   0.000      .715963    1.347759
   post_threat |  -.5859691   .2533255    -2.31   0.023    -1.089648   -.0822901
       hour_sq |  -.0009992    .000547    -1.83   0.071    -.0020867    .0000884
          hour |   .0232159   .0146581     1.58   0.117    -.0059284    .0523601
    gg_hashtag |  -.1959602   .1216622    -1.61   0.111    -.4378573    .0459369
         gg_at |    -.39355    .100968    -3.90   0.000    -.5943013   -.1927986
      gg_reply |  -1.768775   .3040242    -5.82   0.000    -2.373256   -1.164293
         gg_rt |          0  (omitted)
       gg_http |   .2755708   .0697874     3.95   0.000     .1368147    .4143269
         _cons |   3.247067   .1744646    18.61   0.000     2.900185     3.59395
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
      userid |        86          86           0    *|
       date2 |        30           0          30     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA9.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(post_opo_ada humor post_humor criticism post_crit
> icism threat post_threat) stats(N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles t
> itle("Violent attack and Twitter engagement: Negative campaiging effect") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA9.tex)

. 
. 
. 
. **** Table A20 ****
. 
. 
. 
. label variable opposition "Opposition (dummy)"

. label variable log_rt "Retweets (log)"

. label variable log_fav "Likes (log)"

. label variable log_engagement "Engagement (log)"

. 
. gen totaleng = favcount + rt_count

. 
. label variable rt_count "Retweets"

. label variable favcount "Likes"

. label variable totaleng "Engagement"

. 
. 
. replace humor = 0 if humor==.
(0 real changes made)

. replace threat = 0 if threat==.
(0 real changes made)

. replace criticism = 0 if criticism==.
(0 real changes made)

. 
. label variable humor "Humorous criticism (dummy)"

. label variable criticism "Explicit criticism (dummy)"

. label variable threat "Threat (dummy)"

. label variable mentionOther "Mention rival (dummy)"

. label variable post_ada "Post assassination (dummy)"

. 
. sutex opposition log_rt log_fav log_engagement rt_count favcount totaleng humor threat criticism mentionOther post_ada, minmax label
> s title("Summary statistics for tweets \label{tab:taba20}") file("${PathTab}TableA20.tex") replace
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA20.tex saved

. 
end of do-file

. do "7_twitter_public.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: All tables and graphs 
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. 
. * Path 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales" {
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}API2_polandstata.dta", clear

. 
. 
. gen date = substr(created_at,1,10)

. gen month = substr(date,6,2)

. gen year = substr(date,1,4)

. gen day = substr(date,9,2)

. gen hour = substr(created_at,12,2)

. destring(month), replace
month: all characters numeric; replaced as byte

. destring(year), replace
year: all characters numeric; replaced as int

. destring(day), replace
day: all characters numeric; replaced as byte

. destring(hour), replace
hour: all characters numeric; replaced as byte

. gen time = (year-2012)*12 + month

. gen timeD = (year-2012)*365 + (month==2)*31 + (month==3)*(31+28) + (month==4)*(31*2 + 28) + (month==5)*(31*2 + 28 +30) + (month==6)*
> (31*3 + 28 +30) + (month==7)*(31*3 + 28 + 30*2) + (month==8)*(31*4 + 28 + 30*2) + (month==9)*(31*5 + 28 + 30*2) + (month==10)*(31*5 
> + 28 + 30*3) + (month==11)*(31*6 + 28 + 30*3) + (month==12)*(31*6 + 28 + 30*4) + day + 1*((month>2 & year==2012)|(year>2012))

. gen week = round(timeD/7)

. gen date2 = mdy(month, day, year)

. 
. gen gg_rt = strpos(text, "rt ") == 1

. 
. gen log_rt = log(rt_count + 1)

. gen log_fav = log(favcount + 1)

. gen log_engagement = log(rt_count + favcount + 1)

. egen userid = group(screen_name)

. bysort date2 userid: gen N_du = _N

. 
. 
. gen dayC_ada = date2 - 21562

. gen dayC_sq_ada = dayC_ada*dayC_ada

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada

. gen post_ada = date2>=21562

. gen dayC_post_ada = dayC_ada*post_ada

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada

. 
. gen hour_sq = hour*hour

. gen hour_cu = hour*hour*hour

. 
. bysort userid: gen n_u = _n

. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21562

. foreach targetdate in 21562 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(235,848 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21562

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
. 
. gen gg_hashtag = strpos(text, "#")>0

. gen gg_at = strpos(text, "@")>0

. gen gg_http = strpos(text, "http")>0

. 
. gen text_l = lower(text)
(143 missing values generated)

. replace text = text_l
(221,916 real changes made)

. drop text_l

. replace text = subinstr(text, "á", "a",.) 
(619 real changes made)

. replace text = subinstr(text, "é", "e",.) 
(1,117 real changes made)

. replace text = subinstr(text, "í", "i",.) 
(571 real changes made)

. replace text = subinstr(text, "ó", "o",.) 
(45,900 real changes made)

. replace text = subinstr(text, "ú", "u",.)
(169 real changes made)

. replace text = subinstr(text, "ñ", "nh",.)
(232 real changes made)

. replace text = subinstr(text, ",", " ",.)
(72,635 real changes made)

. replace text = subinstr(text, `"""',  "", .)
(7,124 real changes made)

. replace text = subinstr(text, ".", " ",.) 
(150,150 real changes made)

. replace text = subinstr(text, "-", " ",.)
(19,236 real changes made)

. replace text = subinstr(text, "!", " ",.) 
(23,263 real changes made)

. replace text = subinstr(text, "/", " ",.)
(80,938 real changes made)

. replace text = subinstr(text, "…", " ",.)
(0 real changes made)

. replace text = subinstr(text, ":", " ",.)
(89,607 real changes made)

. replace text = subinstr(text, ";", " ",.)
(6,304 real changes made)

. replace text = subinstr(text, "#", "",.)
(32,771 real changes made)

. replace text = subinstr(text, "@", "",.)
(153,855 real changes made)

. forvalues i = 1(1)7 {
  2.         replace text = subinstr(text, "  ", " ",.)
  3. }
(173,768 real changes made)
(113,438 real changes made)
(13,732 real changes made)
(5,030 real changes made)
(1,821 real changes made)
(370 real changes made)
(15 real changes made)

. 
. replace text = " " + text
(235,848 real changes made)

. gen threat =  strpos(text, "nienawi") + strpos(text, "wrogo") + strpos(text, "pogard") + strpos(text, " odraza") + strpos(text, "obr
> zydzenie") + strpos(text, "agresja") + strpos(text, "hejt")  + strpos(text, "grozi")  + strpos(text, "grob")  + strpos(text, "zagroe
> n")  + strpos(text, "niebezpieczestw")  >0

. gen unity =  strpos(text, "jedno ") + strpos(text, "jednoi") + strpos(text, "solidarno") + strpos(text, "porozumienie") + strpos(tex
> t, "pojedna") + strpos(text, "blisko ") + strpos(text, "bliskoi") >0

. gen criticism =  strpos(text, "krytyczn") + strpos(text, "wadliw") + strpos(text, "nieodpowiedzi") + strpos(text, "oszustw") + strpo
> s(text, "kamstw") + strpos(text, "nielegal") + strpos(text, "nieuczciw") + strpos(text, "niemoraln") + strpos(text, " mylc") + strpo
> s(text, " bedn")  >0

. 
. gen post_threat = post_ada * threat

. gen post_unity = post_ada * unity

. gen post_criticism = post_ada * criticism

. 
. label variable post_criticism "Post x Explicit criticism"

. label variable post_unity "Post x Call for unity"

. label variable post_threat "Post x Threat"

. 
. gen gg_adamowicz = strpos(text, "adamowicz")>0

. 
. replace text = " " + text
(235,848 real changes made)

. gen opMentions = strpos(text," rzd") + strpos(text," min ") + strpos(text,"minist") + strpos(text,"wicemin") + strpos(text,"pis ") +
>  strpos(text,"tvp") + strpos(text,"andruszkiewicz") + strpos(text,"berni_krynick") + strpos(text,"budet") + strpos(text,"premier") +
>  strpos(text,"morawieck") + strpos(text,"pisowsk") + strpos(text,"macierewicz") + strpos(text,"kaczysk") + strpos(text," msz ") + st
> rpos(text,"tygodnik_sieci") + strpos(text,"smolesk") + strpos(text,"misiewicz") + strpos(text,"tasmykaczynsk") + strpos(text,"glapis
> ki") + strpos(text,"glapinski") + strpos(text,"szydo") + strpos(text,"ziobro") + strpos(text,"zieliski") + strpos(text,"gowin") + st
> rpos(text,"jbrudzinski") + strpos(text,"drelich") + strpos(text,"terlecki") + strpos(text,"sdownictw") + strpos(text,"pisorgpl") + s
> trpos(text," nbp ") + strpos(text,"wicemarsz") + strpos(text," cba ") + strpos(text,"patryk jaki") + strpos(text,"policj") + strpos(
> text,"czaputowicz") + strpos(text,"gliski")

. replace opMentions = opMentions>0
(16,606 real changes made)

. gen govMentions = strpos(text," opozycj") + strpos(text,"bzdrojewski") + strpos(text," lewic") + strpos(text,"olejnik") + strpos(tex
> t," lewicow") + strpos(text," biedro") + strpos(text," lewac") + strpos(text," psl_") + strpos(text," psl ") + strpos(text,"gazwyb")
>  + strpos(text," nowick") + strpos(text," sikorsk") + strpos(text,"schetyn") + strpos(text," neuman") + strpos(text,"tomasz_lis") + 
> strpos(text,"tomasz lis") + strpos(text," lis_tomasz") + strpos(text," tusk") + strpos(text," platform") + strpos(text,"trzaskowski_
> ") + strpos(text,"platforma_org") + strpos(text,"klubnauer") + strpos(text," gw_") + strpos(text,"gazetawyborcza") + strpos(text,"tv
> n24")

. replace govMentions = govMentions>0
(6,164 real changes made)

. replace govMentions = govMentions + strpos(text,"platforma_org")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(138 real changes made)

. replace govMentions = govMentions + strpos(text,"arlukowicz")>0
(54 real changes made)

. replace govMentions = govMentions + strpos(text,"bbudka")>0
(133 real changes made)

. replace govMentions = govMentions + strpos(text,"andrzejhalicki")>0
(63 real changes made)

. replace govMentions = govMentions + strpos(text,"slawekneumann")>0
(118 real changes made)

. replace govMentions = govMentions + strpos(text,"schetynadlapo")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mkierwinski")>0
(76 real changes made)

. replace govMentions = govMentions + strpos(text,"jangrabiec")>0
(31 real changes made)

. replace govMentions = govMentions + strpos(text,"achybicka")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"protasiewiczj")>0
(2 real changes made)

. replace govMentions = govMentions + strpos(text,"zpawlowicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"trzaskowski_")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"tomaszlenz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"hannagw")>0
(78 real changes made)

. replace govMentions = govMentions + strpos(text,"asia_mucha")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"niesiolowskis")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"bbukiewicz")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"c_grabarczyk")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"ikatarasinska")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"ctomczyk")>0
(45 real changes made)

. replace govMentions = govMentions + strpos(text,"sowamarek")>0
(2 real changes made)

. replace govMentions = govMentions + strpos(text,"ireneuszras")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"kopacz_ewa")>0
(16 real changes made)

. replace govMentions = govMentions + strpos(text,"m_k_blonska")>0
(14 real changes made)

. replace govMentions = govMentions + strpos(text,"adam_korol")>0
(8 real changes made)

. replace govMentions = govMentions + strpos(text,"pomaska")>0
(49 real changes made)

. replace govMentions = govMentions + strpos(text,"henryka_henia50")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"miroslawanykiel")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"marianzembala")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"urszulaaugustyn")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"kr_szumilas")>0
(2 real changes made)

. replace govMentions = govMentions + strpos(text,"okladrewnowicz")>0
(15 real changes made)

. replace govMentions = govMentions + strpos(text,"mswitczak")>0
(12 real changes made)

. replace govMentions = govMentions + strpos(text,"jakubrutnicki")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"mjanyska")>0
(14 real changes made)

. replace govMentions = govMentions + strpos(text,"ziolkowskiszym")>0
(5 real changes made)

. replace govMentions = govMentions + strpos(text,"waldydzikowski")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"slawomirnitras")>0
(47 real changes made)

. replace govMentions = govMentions + strpos(text,"krzysztofbrejza")>0
(140 real changes made)

. replace govMentions = govMentions + strpos(text,"arkadiuszmyrcha")>0
(34 real changes made)

. replace govMentions = govMentions + strpos(text,"mwielichowska")>0
(11 real changes made)

. replace govMentions = govMentions + strpos(text,"gajewska_kinga")>0
(10 real changes made)

. replace govMentions = govMentions + strpos(text,"newsplatforma")>0
(73 real changes made)

. replace govMentions = govMentions + strpos(text,"grzegorzfurgo")>0
(16 real changes made)

. replace govMentions = govMentions + strpos(text,"bborusewicz")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"tomaszsiemoniak")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"rtyszkiewicz")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"jaroslawduda")>0
(1 real change made)

. replace govMentions = govMentions + strpos(text,"zdzislaw_gawlik")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"arturgierada")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"a_marchewka")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"jacek_protas")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"wojciechsaluga")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"wslugocki")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"hannazdanowska")>0
(38 real changes made)

. replace govMentions = govMentions + strpos(text,"prezydentzuk")>0
(12 real changes made)

. replace govMentions = govMentions + strpos(text,"izabela_debska")>0
(4 real changes made)

. replace govMentions = govMentions + strpos(text,"tadeuszzwiefka")>0
(0 real changes made)

. replace govMentions = govMentions + strpos(text,"donaldtusk")>0
(204 real changes made)

. replace opMentions = opMentions + strpos(text,"piotr_naimski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"pisorgpl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"morawieckim")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"jbrudzinski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"patrykjaki")>0
(104 real changes made)

. replace opMentions = opMentions + strpos(text,"andzyberto")>0
(183 real changes made)

. replace opMentions = opMentions + strpos(text,"kurskipl")>0
(364 real changes made)

. replace opMentions = opMentions + strpos(text,"piotrglinski")>0
(114 real changes made)

. replace opMentions = opMentions + strpos(text,"jaroslaw_gowin")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"stkarczewski")>0
(20 real changes made)

. replace opMentions = opMentions + strpos(text,"marekkuchcinski")>0
(31 real changes made)

. replace opMentions = opMentions + strpos(text,"beataszydlo")>0
(78 real changes made)

. replace opMentions = opMentions + strpos(text,"beatamk")>0
(236 real changes made)

. replace opMentions = opMentions + strpos(text,"e_rafalska")>0
(39 real changes made)

. replace opMentions = opMentions + strpos(text,"krystpawlowicz")>0
(185 real changes made)

. replace opMentions = opMentions + strpos(text,"ziobropl")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"beatakempa_kprm")>0
(3 real changes made)

. replace opMentions = opMentions + strpos(text,"mblaszczak")>0
(44 real changes made)

. replace opMentions = opMentions + strpos(text,"macierewicz_a")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"elzbietawitek")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"_annazalewska")>0
(24 real changes made)

. replace opMentions = opMentions + strpos(text,"michaldworczyk")>0
(11 real changes made)

. replace opMentions = opMentions + strpos(text,"latostomasz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"slawzawislak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"wassermann_ma")>0
(17 real changes made)

. replace opMentions = opMentions + strpos(text,"arekmularczyk")>0
(6 real changes made)

. replace opMentions = opMentions + strpos(text,"w_bernacki")>0
(1 real change made)

. replace opMentions = opMentions + strpos(text,"mareksuski")>0
(15 real changes made)

. replace opMentions = opMentions + strpos(text,"akosztowniak")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"minenergii")>0
(27 real changes made)

. replace opMentions = opMentions + strpos(text,"a_czartoryski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mariuszkaminsk")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"sasinjacek ?")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"bogdan_rzonca")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejszlachta")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mrirw_gov_pl")>0
(7 real changes made)

. replace opMentions = opMentions + strpos(text,"d_piontkowski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"mkidn_gov_pl")>0
(12 real changes made)

. replace opMentions = opMentions + strpos(text,"stanislaw_szwed")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szymongizynski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"izabelakloc")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"andrzejduda")>0
(562 real changes made)

. replace opMentions = opMentions + strpos(text,"annamkrupka")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"iwonaarent")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"joannalichocka")>0
(9 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldczarneck3")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"tadeuszdziuba")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"zagorskimarek")>0
(8 real changes made)

. replace opMentions = opMentions + strpos(text,"marekgrobarczyk")>0
(2 real changes made)

. replace opMentions = opMentions + strpos(text,"amadamczyk")>0
(7 real changes made)

. replace opMentions = opMentions + strpos(text,"jerzykwiecinski")>0
(9 real changes made)

. replace opMentions = opMentions + strpos(text,"jemilewicz")>0
(33 real changes made)

. replace opMentions = opMentions + strpos(text,"witoldbanka")>0
(12 real changes made)

. replace opMentions = opMentions + strpos(text,"jczaputowicz")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"szumowskilukasz")>0
(20 real changes made)

. replace opMentions = opMentions + strpos(text,"j_kopcinska")>0
(9 real changes made)

. replace opMentions = opMentions + strpos(text,"profkarski")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"ryszardterlecki")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"r_czarnecki")>0
(66 real changes made)

. replace opMentions = opMentions + strpos(text,"grzegorzczelej")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"anita_cz")>0
(9 real changes made)

. replace opMentions = opMentions + strpos(text,"ministerjurgiel")>0
(0 real changes made)

. replace opMentions = opMentions + strpos(text,"cymanskitadeusz")>0
(7 real changes made)

. replace opMentions = opMentions + strpos(text,"jacekzalek")>0
(5 real changes made)

. 
. 
. 
. 
. ***** TABLE A27 *****
. 
. 
. eststo clear

. eststo, title("Engagement"): reghdfe log_engagement threat post_threat criticism post_criticism unity post_unity hour_sq hour gg_has
> htag gg_at gg_rt gg_http gg_adamowicz govMentions opMentions  if timeWindow3_ada & lang=="pl",  cl(userid) abs( date2)
(MWFE estimator converged in 1 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =    166,347
Absorbing 1 HDFE group                            F(  14,   6430) =      14.38
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0525
                                                  Adj R-squared   =     0.0523
                                                  Within R-sq.    =     0.0514
Number of clusters (userid)  =      6,431         Root MSE        =     0.9720

                               (Std. Err. adjusted for 6,431 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
log_engagement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        threat |   .3839452   .0468252     8.20   0.000     .2921523    .4757382
   post_threat |  -.1028562    .050978    -2.02   0.044    -.2027899   -.0029224
     criticism |   .3530347    .091868     3.84   0.000     .1729428    .5331266
post_criticism |  -.1311038   .1098749    -1.19   0.233    -.3464952    .0842876
         unity |   .2197378   .0657095     3.34   0.001     .0909253    .3485502
    post_unity |   .1425503   .0800369     1.78   0.075    -.0143488    .2994494
       hour_sq |  -.0003676    .000234    -1.57   0.116    -.0008262    .0000911
          hour |   .0130281   .0055175     2.36   0.018     .0022119    .0238442
    gg_hashtag |   .0715556   .0745108     0.96   0.337    -.0745104    .2176216
         gg_at |  -.1551213    .060793    -2.55   0.011    -.2742959   -.0359467
         gg_rt |          0  (omitted)
       gg_http |   .3234731   .0664353     4.87   0.000     .1932379    .4537084
  gg_adamowicz |   .3770759   .0564451     6.68   0.000     .2664248     .487727
   govMentions |   .1027086   .0326409     3.15   0.002     .0387216    .1666957
    opMentions |   .2020326    .035807     5.64   0.000      .131839    .2722262
         _cons |   .6089505    .076613     7.95   0.000     .4587634    .7591375
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
       date2 |        30           0          30     |
-----------------------------------------------------+
(est1 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Retweets"): reghdfe log_rt threat post_threat criticism post_criticism unity post_unity hour_sq hour gg_hashtag gg_at
>  gg_rt gg_http gg_adamowicz govMentions opMentions  if timeWindow3_ada & lang=="pl", cl(userid) abs( date2)
(MWFE estimator converged in 1 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =    166,347
Absorbing 1 HDFE group                            F(  14,   6430) =      10.64
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0904
                                                  Adj R-squared   =     0.0902
                                                  Within R-sq.    =     0.0888
Number of clusters (userid)  =      6,431         Root MSE        =     0.5061

                               (Std. Err. adjusted for 6,431 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
        log_rt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        threat |   .1099344   .0409214     2.69   0.007     .0297148     .190154
   post_threat |   .0616091   .0409194     1.51   0.132    -.0186064    .1418247
     criticism |    .274649   .0656316     4.18   0.000     .1459891    .4033089
post_criticism |  -.0959321    .070289    -1.36   0.172    -.2337219    .0418578
         unity |   .0978755   .0348421     2.81   0.005     .0295733    .1661777
    post_unity |   .1564523   .0527482     2.97   0.003     .0530482    .2598563
       hour_sq |  -.0002074   .0000876    -2.37   0.018     -.000379   -.0000357
          hour |   .0053319   .0020005     2.67   0.008     .0014103    .0092535
    gg_hashtag |   .0205423   .0333087     0.62   0.537    -.0447539    .0858385
         gg_at |  -.1252159   .0318295    -3.93   0.000    -.1876122   -.0628195
         gg_rt |          0  (omitted)
       gg_http |   .2196959   .0336032     6.54   0.000     .1538224    .2855694
  gg_adamowicz |   .2361185   .0341822     6.91   0.000       .16911    .3031271
   govMentions |   .0827673   .0192875     4.29   0.000     .0449574    .1205772
    opMentions |   .1699544    .025383     6.70   0.000     .1201953    .2197135
         _cons |   .1174845   .0313331     3.75   0.000     .0560611    .1789079
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
       date2 |        30           0          30     |
-----------------------------------------------------+
(est2 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. eststo, title("Likes"): reghdfe log_fav threat post_threat criticism post_criticism unity post_unity hour_sq hour gg_hashtag gg_at g
> g_rt gg_http gg_adamowicz govMentions opMentions  if timeWindow3_ada  & lang=="pl", cl(userid) abs( date2)
(MWFE estimator converged in 1 iterations)
note: gg_rt omitted because of collinearity

HDFE Linear regression                            Number of obs   =    166,347
Absorbing 1 HDFE group                            F(  14,   6430) =      13.54
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0437
                                                  Adj R-squared   =     0.0434
                                                  Within R-sq.    =     0.0426
Number of clusters (userid)  =      6,431         Root MSE        =     0.9351

                               (Std. Err. adjusted for 6,431 clusters in userid)
--------------------------------------------------------------------------------
               |               Robust
       log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
        threat |   .3655584   .0477876     7.65   0.000     .2718787     .459238
   post_threat |  -.1108208   .0486784    -2.28   0.023    -.2062466   -.0153949
     criticism |   .3103788   .0856174     3.63   0.000     .1425401    .4782175
post_criticism |  -.1205711   .1046811    -1.15   0.249    -.3257808    .0846386
         unity |   .1994794   .0630712     3.16   0.002     .0758389      .32312
    post_unity |   .1284119   .0761528     1.69   0.092    -.0208729    .2776967
       hour_sq |  -.0003433   .0002265    -1.52   0.130    -.0007873    .0001008
          hour |   .0125049   .0053884     2.32   0.020     .0019417     .023068
    gg_hashtag |   .0673414   .0688875     0.98   0.328     -.067701    .2023838
         gg_at |  -.1396325   .0567986    -2.46   0.014    -.2509767   -.0282883
         gg_rt |          0  (omitted)
       gg_http |   .2727219   .0610022     4.47   0.000     .1531372    .3923066
  gg_adamowicz |   .3497349   .0536115     6.52   0.000     .2446386    .4548312
   govMentions |    .090514   .0306544     2.95   0.003     .0304212    .1506069
    opMentions |   .1754203   .0338866     5.18   0.000     .1089912    .2418494
         _cons |    .584429   .0733164     7.97   0.000     .4407044    .7281537
--------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
       date2 |        30           0          30     |
-----------------------------------------------------+
(est3 stored)

. estadd local dFE = "Yes"

added macro:
                e(dFE) : "Yes"

. estadd local pFE = "Yes"

added macro:
                e(pFE) : "Yes"

. estadd local controls = "Yes"

added macro:
           e(controls) : "Yes"

. esttab using "${PathTab}TableA27.tex", b(3) se(3) star(* 0.1 ** 0.05 *** 0.01) keep(threat post_threat criticism post_criticism unit
> y post_unity) stats(N pFE dFE controls, fmt(0) labels("N" "Day FE" "Politician FE" "Controls")) label nodepvar mtitles title("Violen
> t attack and Twitter engagement: Negative tweets effect (general population)") replace nonotes postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA27.tex)

. 
. 
. 
. 
. 
end of do-file

. do "8_news_freq.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: Table A26
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. ************************************
. * Path 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales" {
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}\news_final.dta"

. 
. preserve

. keep if neutral
(1,780 observations deleted)

. 
. txttool text, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   83894 unique words, 548065 total words
Output:  51201 unique words, 436488 total words
Total time: 84.464 seconds

. ngram text, degree(1) threshold(100) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in  t_adamowicz t_adamowicza t_andrzej t_aplikacji t_b t_bd t_bdzie t_bdziemi t_bezpieczestwa t_bya t_byi t_byo t_celu 
> t_centrum t_chc t_chce t_chodzi t_chwili t_cz t_cza t_czasi t_czasu t_czci t_czwartek t_czym t_czynnoci t_decyzj t_decyzji t_dni t_d
> nia t_doda t_dodaa t_domu t_doszo t_dotyczc t_dotyczi t_drugi t_ds t_dwch t_dyrektor t_dzi t_dziaa t_dziaalnoci t_dziaania t_dzie t_
> dzieci t_dziki t_e t_ebi t_europejskiej t_finau t_funkcjonariusz t_gdaska t_gdasku t_godz t_godzin t_godzini t_grozi t_grudnia t_gru
> pi t_informacj t_informacji t_informuj t_j t_jarosaw t_jednego t_jednej t_jednoczeni t_jednym t_jeeli t_jeli t_jestemi t_ju t_kadi t
> _kar t_kilku t_kolei t_kolejn t_komendi t_komisji t_komunikaci t_konferencji t_koniec t_kontroli t_kraju t_ktr t_ktra t_ktre t_ktreg
> o t_ktrej t_ktry t_ktrych t_ktrym t_ktrzy t_lata t_latach t_latek t_ledztwa t_ledztwo t_letni t_letniego t_ludzi t_maj t_mami t_mczy
> zn t_mczyzna t_mediach t_mg t_mia t_miaa t_miai t_miao t_miasta t_midzi t_mie t_miejsc t_miejsca t_miejscu t_mierci t_miesic t_miesi
> ci t_mieszkacw t_min t_minist t_ministra t_mln t_moe t_mog t_moliwoci t_mona t_mwi t_mwia t_nalei t_napisa t_nastpni t_new t_ni t_ni
> edziel t_nienawici t_noci t_noem t_now t_np t_obecni t_oceni t_ochroni t_ok t_okazao t_okoo t_okrgowej t_opini t_organizacji t_osb t
> _osob t_osoba t_osobi t_owiadczi t_p t_partii t_pastwa t_paw t_pawa t_pi t_pierwsz t_pierwszi t_piotr t_pitek t_pniej t_pocztku t_po
> da t_podkr t_podkrelia t_podstawi t_poinformowa t_poinformowaa t_policj t_policja t_policji t_polityk t_polsat t_polsc t_polsk t_pol
> ska t_polski t_polskich t_polskiego t_polskiej t_pomoc t_pomoci t_ponadto t_poniedziaek t_poniewa t_postpowani t_postpowania t_poudn
> iu t_powiedzi t_powiedzia t_powiedziaa t_powodu t_pozbawienia t_prac t_praci t_pracownikw t_prasowej t_prasowi t_prawa t_prawo t_pre
> mier t_premiera t_preze t_prezesa t_prezyd t_prezydenta t_prezydentem t_proc t_programi t_projekt t_prokur t_prokuratura t_prokuratu
> ri t_prowadzi t_przeciwko t_przedstawiciel t_przekaza t_przestpstwa t_przypadku t_przypomnia t_przyzna t_publicznej t_publicznych t_
> pytani t_r t_radi t_ramach t_rano t_raz t_razem t_razi t_red t_rnych t_rod t_rodkw t_rodzin t_rodzini t_rok t_rozmowi t_rwnie t_rzdu
>  t_rzecz t_rzeczniczka t_rzecznik t_scen t_sd t_sdu t_si t_sobot t_sowa t_sposb t_spotkania t_spraw t_sprawa t_sprawdna t_sprawi t_s
> prawiedliwoci t_stan t_stani t_stanu t_stefana t_stroni t_stwierdzi t_stycznia t_subi t_swoich t_swoim t_swoj t_swojego t_swojej t_s
> ytuacj t_sytuacja t_sytuacji t_szef t_szefa t_szpitala t_take t_takich t_takiej t_telefon t_temat t_temu t_tereni t_trafi t_trakci t
> _trzech t_trzy t_twitterz t_twj t_ty t_tzw t_udao t_udzia t_ul t_urzdu t_ustal t_ustawi t_uwag t_wadz t_wani t_warszawi t_wczeniej t
> _wedug t_wic t_wicej t_wieczorem t_wielkiej t_wizienia t_wniosek t_wobec t_woj t_wolnoci t_wop t_wraz t_wrd t_ws t_wskaza t_wtorek t
> _wwcza t_wyjani t_wyjania t_wynika t_wyniku t_wysokoci t_wzgldu t_ycia t_ycie t_zakresi t_zapewni t_zapowiedzia t_zarwno t_zarzut t_
> zarzuti t_zatrzymani t_zaznaczi t_zdaniem t_zdarzenia t_zdrowia t_zgodni t_zmar t_zmiani t_zoi t_zosta t_zostaa t_zostai t_zostani t
> _zostao t_zrobi t_zwizku t_zwrci {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. 
. keep in 1
(1,440 observations deleted)

. keep S_*

. br

. 
. restore

. 
. 
. preserve

. keep if progov
(2,328 observations deleted)

. 
. txttool text, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   55751 unique words, 278533 total words
Output:  35386 unique words, 199901 total words
Total time: 24.454 seconds

. ngram text, degree(1) threshold(50) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in t_adamowicz t_adamowicza t_andrzej t_ataku t_b t_bd t_bdzie t_bdziemi t_bezpieczestwa t_biura t_bya t_byego t_byi t_
> byo t_cai t_celu t_centrum t_charakterz t_chc t_chce t_chodzi t_chwili t_cigu t_cz t_cza t_czasi t_czasu t_czci t_czteri t_czwartek 
> t_czym t_czynnoci t_decyzj t_decyzji t_dni t_dnia t_doda t_dodaa t_dopiero t_doszo t_dotyczc t_dotyczi t_ds t_duda t_dwch t_dyrektor
>  t_dzi t_dziaa t_dziaalnoci t_dziaania t_dzie t_dzieci t_dziki t_e t_ebi t_europejskiej t_finau t_funkcjonariusz t_gazeta t_gdaska t
> _gdasku t_godz t_grozi t_grudnia t_grupi t_informacj t_informacji t_informuj t_j t_jarosaw t_jednego t_jednej t_jednoczeni t_jednym 
> t_jeeli t_jeli t_ju t_kadi t_kar t_kilku t_kolei t_kolejn t_komisji t_konferencji t_koniec t_kontroli t_krajowej t_kraju t_krzysztof
>  t_ktr t_ktra t_ktre t_ktrego t_ktrej t_ktry t_ktrych t_ktrym t_ktrzy t_lata t_latach t_ledczi t_ledztwa t_ledztwo t_letni t_letnieg
> o t_ludzi t_lutego t_maj t_mami t_mateusz t_mczyzn t_mczyzna t_media t_mia t_miaa t_miai t_miao t_miasta t_midzi t_mie t_miejsc t_mi
> ejsca t_miejscu t_mieli t_mierci t_miesic t_miesici t_min t_minist t_ministra t_mln t_moe t_mog t_moliwoci t_mona t_morawiecki t_mwi
>  t_nadziej t_najmniej t_nalei t_napisa t_nasw t_ni t_niedziel t_nienawici t_noci t_noem t_np t_obecni t_obroni t_oceni t_ochroni t_o
> k t_okoo t_okrgowej t_osb t_osob t_osoba t_osobi t_ostatni t_ostatnich t_p t_partii t_pastwa t_paw t_pawa t_pi t_pierwsz t_pierwszi 
> t_piotr t_pitek t_platformi t_pniej t_pocztku t_poda t_podaj t_podkr t_poinformowa t_poinformowaa t_polakw t_policj t_policja t_poli
> cji t_polityk t_politykw t_polsc t_polsk t_polska t_polski t_polskich t_polskiego t_polskiej t_polub t_pomoc t_pomoci t_poniedziaek 
> t_poniewa t_poowi t_portal t_pose t_postpowania t_poudniu t_powiedzi t_powiedzia t_powiedziaa t_powodu t_pozbawienia t_prac t_praci 
> t_pracownikw t_prawa t_prawo t_premier t_premiera t_preze t_prezesa t_prezyd t_prezydenta t_proc t_proce t_program t_programu t_prok
> ur t_prokuratura t_prokuraturi t_prowadzi t_przeciwko t_przestpstwa t_przypadku t_przypomnia t_przyzna t_publicznej t_pytani t_r t_r
> adi t_ramach t_rano t_raz t_razem t_razi t_rod t_rodzin t_rodzini t_rok t_rozmowi t_rp t_rwnie t_rzdu t_rzecz t_rzeczniczka t_rzeczn
> ik t_scen t_sd t_sdu t_si t_siebi t_sobot t_sowa t_sposb t_spraw t_sprawa t_sprawi t_sprawiedliwoci t_stan t_stani t_stefan t_stefan
> a t_stroni t_stwierdzi t_stycznia t_subi t_swoich t_swoim t_swoj t_sytuacji t_szef t_szefa t_szpitala t_take t_takich t_temat t_temu
>  t_tereni t_tomasz t_trafi t_trakci t_trzech t_trzy t_tvp t_tvpinfo t_twitterz t_ty t_tzw t_udao t_udzia t_ul t_ustal t_ustawi t_uwa
> g t_wadz t_wani t_warszawi t_wartoci t_wczeniej t_wedug t_wiadomo t_wic t_wicej t_wieczorem t_wielkiej t_wieszwiecej t_wieszwiecejpo
> lub t_wizienia t_wniosek t_wobec t_wolnoci t_wop t_wraz t_wrd t_wskaza t_wtargn t_wtorek t_wwcza t_wyborach t_wyborczej t_wyjani t_w
> yjania t_wynika t_wyniku t_wyrok t_wysokoci t_wzgldu t_ycia t_ycie t_zabjstwa t_zakresi t_zapewni t_zarzut t_zarzuti t_zatrzymani t_
> zaznaczi t_zdaniem t_zdarzenia t_zdrowia t_zgodni t_zmar t_zoi t_zosta t_zostaa t_zostai t_zostani t_zostao t_zotych t_zrobi t_zwizk
> u t_zwrci {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. keep in 1
(892 observations deleted)

. keep S_*

. 
. restore

. 
. preserve

. keep if proopp
(2,334 observations deleted)

. 
. txttool text, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   77616 unique words, 741361 total words
Output:  47524 unique words, 563540 total words
Total time: 78.226 seconds

. ngram text, degree(1) threshold(100) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in t_adamowicz t_adamowicza t_andrzej t_andrzeja t_apeluj t_aresztu t_art t_artykuw t_ataku t_auto t_autorskim t_b t_ba
> tyckiej t_bd t_bdzie t_bdziemi t_bezpieczestwa t_biedronia t_bispl t_bocianim t_broni t_burz t_bya t_byi t_byo t_c t_ca t_cai t_caym
>  t_celem t_celsjusza t_centrum t_chce t_chcia t_chcielibyci t_chodzi t_chwili t_chyba t_ciao t_ciek t_cigu t_copyright t_cz t_cza t_
> czasi t_czasu t_czci t_czego t_czekami t_czerwonego t_czerwoni t_czowiek t_czowieka t_czwartek t_czym t_czynnoci t_czytaj t_czytajda
> lej t_da t_dalej t_dalsz t_dane t_decyzj t_deszczu t_dni t_dnia t_dniach t_dobr t_doda t_dodaa t_dodaj t_domu t_doradca t_doskonal t
> _doszo t_dotyczc t_dowiedzia t_drodz t_drogi t_drugi t_dudi t_duo t_dwch t_dwjk t_dynamicznej t_dyskotek t_dzi t_dziaa t_dziaania t_
> dzie t_dzieci t_dziwni t_dziwnowi t_e t_ebi t_eglugi t_europejskiej t_fina t_finau t_funduj t_gdask t_gdaska t_gdasku t_gniazdem t_g
> o t_godzin t_godzini t_godzinnego t_gospodarki t_grozi t_grudnia t_grzegorza t_gwny t_head t_imi t_informacj t_informacji t_informuj
>  t_innymi t_instytut t_intensywnymi t_internetowej t_internetowych t_j t_jacek t_jadci t_jaka t_jarosaw t_jednego t_jednej t_jednym 
> t_jeeli t_jeli t_jestemi t_ju t_k t_kadi t_kamienica t_kampani t_kierowca t_kierownictwo t_kobieta t_kogo t_konferencji t_koniec t_k
> onstytucyjnego t_korzystani t_koszt t_krajowych t_kraju t_krtk t_krzysztof t_ktr t_ktra t_ktre t_ktrego t_ktrej t_ktry t_ktrych t_kt
> rym t_ktrzy t_kupi t_kwietnia t_kwot t_lata t_latach t_latek t_latka t_letni t_letniego t_licencyjnej t_liczbi t_lotu t_lotw t_ludzi
>  t_maj t_majc t_maksymalni t_mami t_marek t_materiaw t_mateusz t_mczyzn t_mczyzna t_media t_meteorologii t_mg t_mia t_miaa t_miao t_
> miasta t_micha t_midzi t_mie t_miejsc t_miejscami t_miejscu t_mieli t_mierci t_miesic t_miesici t_mieszkaci t_min t_minist t_miszek 
> t_moe t_moemi t_mog t_mogli t_momenci t_mona t_morawiecki t_musz t_mwi t_mwia t_myl t_n t_nadali t_nadziej t_najbardziej t_najbliszy
> ch t_najpierw t_nakontakt t_nalei t_napisa t_naprawd t_nastpi t_ni t_niebezpiecznymi t_niedziel t_nienawici t_nieoficjalnych t_niest
> eti t_nikt t_noci t_noem t_notowania t_o t_obecni t_obroni t_obywatelskich t_obywatelskiej t_oceni t_ochroni t_odchodzi t_odjecha t_
> odni t_odpowiedzialni t_ogldaj t_okoo t_opadi t_opni t_opowiada t_opublikowano t_organizacji t_orkiestri t_osb t_osoba t_osobi t_ost
> atni t_ostrzega t_partii t_pasaerskosamochodowego t_pastwa t_pastwo t_paw t_pawa t_peen t_pewno t_pewnym t_pi t_pienidz t_pierwszi t
> _pierwszym t_pieszych t_piotr t_piszci t_pitek t_pkt t_pl t_plai t_platformi t_pniej t_pocztku t_podj t_podkr t_podkrela t_podstawi 
> t_pogodi t_pogodowymi t_poinformowa t_poinformowaa t_pojawi t_poka t_pokrewnych t_pokrywa t_polaci t_policj t_policja t_policjantw t
> _policji t_polityci t_polityk t_politykw t_polsc t_polsk t_polska t_polski t_polskiego t_polskiej t_pomoci t_pomorskiem t_poniedziae
> k t_poniewa t_popada t_pose t_postpowania t_potem t_poudniu t_powiedzi t_powiedzia t_powiedziaa t_powiej t_powodu t_prac t_praci t_p
> raw t_prawa t_prawach t_prawidowo t_prawo t_premier t_preze t_prezesa t_prezyd t_prezydenta t_prezydentem t_proczu t_programach t_pr
> ojektowaniu t_prokuratura t_prokuraturi t_promu t_prostu t_prowadzi t_przechodz t_przeci t_przeciwko t_przedstawi t_przejci t_przeka
> za t_przekazuj t_przelotn t_przemoci t_przestpstw t_przestpstwa t_przyczyn t_przypadku t_przyzna t_publicznej t_pytani t_r t_radi t_
> ran t_rano t_raz t_razem t_razi t_rdo t_red t_redakcyjnych t_regionem t_rnych t_roberta t_robi t_rod t_rodzina t_rodzini t_rowerow t
> _rozmawia t_rozmowi t_rozpowszechniani t_rozwi t_rwnie t_rzdowym t_rzdu t_rzecz t_rzeczniczka t_rzecznik t_rzeszowa t_sa t_samochd t
> _sceni t_schetyni t_sd t_sdu t_senatu t_serca t_si t_siebi t_siedzibi t_skali t_sobot t_solidarnoci t_sowa t_sp t_spaceru t_spodziew
> a t_sposb t_spotkani t_spotkania t_spraw t_sprawi t_sprawiedliwoci t_sta t_stan t_stani t_stao t_statusi t_statystyczni t_stefan t_s
> topni t_stosownej t_stou t_straci t_stronach t_stroni t_stwierdzi t_stycznia t_suba t_swoich t_swoim t_swoj t_swojego t_swojej t_syg
> naymateriai t_syska t_sytuacja t_sytuacji t_szeciolatki t_szef t_sznurek t_szpitala t_szpitalu t_szyi t_take t_takich t_takiego t_ta
> kim t_telefoni t_temat t_tematem t_temu t_termometri t_torowisko t_trafi t_trakci t_trudno t_trwa t_trybunau t_trzech t_trzy t_tumac
> zi t_tvn t_tvnpl t_twitterz t_tyle t_tysici t_udao t_uderzi t_udzia t_ugrupowania t_umowi t_upalni t_urzd t_usiad t_ust t_ustawi t_u
> sysza t_uwag t_wadz t_wane t_wani t_warszawi t_wartoci t_waszym t_wci t_wczeniej t_wczeniejszej t_wedug t_wiadomo t_wiata t_wic t_wi
> cej t_wida t_wideo t_wie t_wieci t_wieczorem t_wielka t_wielki t_wielkiej t_wiem t_wiemi t_wiosni t_witecznej t_wizienia t_wniosek t
> _wobec t_wodnej t_wolno t_wolnoci t_wop t_wpatrzeni t_wrd t_wsi t_wskaza t_wskazuj t_wtorek t_wyborach t_wyborcz t_wyglda t_wyjani t
> _wyjania t_wykaz t_wymaga t_wynika t_wyniku t_wyrani t_wysokoci t_ycia t_ycie t_yka t_zaatakowa t_zabjstwa t_zabra t_zabronion t_zac
> hodniopomorski t_zacz t_zaczam t_zadanych t_zagranicznych t_zagrzmi t_zainteresowa t_zamieszczonych t_zanadrzu t_zaopiekowaa t_zapar
> kowan t_zarzut t_zarzuti t_zaskareni t_zastrzega t_zatrzymani t_zauwaaj t_zauwai t_zauwayam t_zauwayli t_zawarcia t_zawieszona t_zaz
> naczi t_zdaniem t_zdarzenia t_zdecydowalimi t_zdrowia t_zebrao t_zgodi t_zgodni t_zjawiskami t_zmar t_zmian t_znale t_zobacz t_zosta
>  t_zostaa t_zostai t_zostani t_zostao t_zotych t_zrobi t_zwizanym t_zwizku t_zwrci {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. keep in 1
(886 observations deleted)

. keep S_*

. 
. restore

. 
. 
. preserve

. keep if neutral
(1,780 observations deleted)

. 
. txttool title, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   7081 unique words, 14257 total words
Output:  6154 unique words, 11976 total words
Total time: 1.928 seconds

. ngram title, degree(1) threshold(10) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in t_adamowicza t_akt t_apeluj t_areszt t_aresztu t_ataku t_b t_bd t_bdzie t_byi t_byo t_cba t_chce t_domu t_dot t_dulk
> iewicz t_dwch t_dzieci t_dzikw t_e t_escap t_gdaska t_gdasku t_grozi t_imgw t_j t_ju t_kaczyskiego t_kierowca t_knf t_komisja t_kopa
> lni t_krakw t_ktry t_latek t_latka t_ledztwo t_letni t_mczyzna t_mia t_miaa t_mierci t_min t_minist t_mln t_moe t_mon t_morawiecki t
> _nauczyci t_nbp t_niegu t_nienawici t_nik t_now t_osb t_oskarenia t_osobi t_p t_pawa t_pi t_poar t_policj t_policja t_polsc t_polska
>  t_polski t_premier t_premiera t_preze t_prezesa t_prezyd t_prezydenta t_proce t_projekt t_prokuratura t_przeciw t_przeciwko t_przes
> tpstwa t_r t_razem t_roomi t_ruszi t_sd t_si t_spotka t_sprawi t_stefan t_stefana t_szef t_szpitalu t_trafi t_trzy t_ty t_ustawi t_v
> at t_warszawi t_wizienia t_wniosek t_wobec t_wop t_ws t_wypadek t_wyrok t_yje t_zabjstwa t_zarzuti t_zatrzymani t_zosta t_zwizku {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. 
. keep in 1
(1,440 observations deleted)

. keep S_*

. br

. 
. restore

. 
. 
. preserve

. keep if progov
(2,328 observations deleted)

. 
. txttool title, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   4635 unique words, 8439 total words
Output:  3925 unique words, 6665 total words
Total time: .882 seconds

. ngram title, degree(1) threshold(8) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in  t_adamowicza t_areszt t_ataku t_bd t_bdquoalarmrdquo t_bdzie t_byi t_byo t_cba t_chce t_dzieci t_dzikoacutew t_e t_
> escap t_gdaska t_gdasku t_grozi t_koszalini t_ktoacuteri t_ledczi t_mczyzna t_mierci t_minist t_moe t_nbp t_nienawici t_ochroni t_op
> inia t_osoacuteb t_osobi t_paacu t_pawa t_pi t_poar t_policja t_polsc t_polski t_premier t_preze t_prezyd t_prezydenta t_projekt t_p
> rokuratura t_psl t_rzecznik t_sd t_si t_sprawa t_sprawi t_stefan t_stefana t_szef t_szefa t_temu t_tragedia t_trwa t_trzech t_trzy t
> _tvp t_ty t_wobec t_wop t_ws t_wyrok t_zarzuti t_zatrzymani t_zmar t_zosta {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. 
. keep in 1
(892 observations deleted)

. keep S_*

. br

. 
. restore

. 
. 
. 
. preserve

. keep if proopp
(2,334 observations deleted)

. 
. txttool title, replace stopwords(${PathData}\polishstopwords.txt) stem
Input:   3532 unique words, 7506 total words
Output:  3001 unique words, 5996 total words
Total time: .799 seconds

. ngram title, degree(1) threshold(8) binarize
Removing stopwords specified in stopwords_en.txt

. 
. foreach varn in  t_adam t_adamowicz t_adamowicza t_andruszkiewicz t_andrzej t_areszt t_atak t_ataku t_bdzie t_byi t_cba t_chce t_dud
> a t_dulkiewicz t_escap t_faktach t_gdask t_gdaska t_jacek t_jerzi t_kaczyski t_kaczyskiego t_kierowca t_komentarz t_konferencja t_ko
> ntrol t_koszalin t_ktry t_latek t_latka t_mia t_mierci t_morawiecki t_mowi t_nbp t_nienawici t_owsiak t_owsiaka t_paw t_pawa t_pi t_
> piotr t_poar t_pogrzeb t_policj t_policja t_polityci t_politykw t_polsc t_polska t_polski t_pozna t_premier t_premiera t_prezesa t_p
> rezyd t_prezydenta t_proce t_projekt t_prokuratura t_roomi t_sd t_sdu t_sejmi t_si t_spotkani t_sprawi t_stefana t_szuka t_tami t_tv
> n t_ustawi t_wop t_wospwtvn t_ws t_wspomina t_wyborach t_wyrok t_yje t_zabjstwo t_zarzuti t_zatrzymani {
  2.         egen S_`varn' = sum(`varn')
  3. }

. 
. 
. keep in 1
(886 observations deleted)

. keep S_*

. br

. 
. restore

. 
end of do-file

. do "9_twitter_figa2.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: Figure A2
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. 
. * Path 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. use "${PathData}tweets_final.dta", clear

. 
. 
. merge m:m screen_name using "${PathData}mp.dta"

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                           239,481  (_merge==3)
    -----------------------------------------

. rename _merge mergeMP

. 
. ***** Coding VARIABLES (general) *****
. 
. gen dOfWeek = mod(date2,7)

. replace dOfWeek = dOfWeek - 2
(239,481 real changes made)

. replace dOfWeek = dOfWeek + 7 if dOfWeek<1
(101,594 real changes made)

. 
. drop gg_rt

. gen gg_rt = strpos(text, "rt ") == 1

. 
. gen gg_adamowicz = strpos(text, "adamowicz")>0

. 
. gen log_rt = log(rt_count + 1)

. gen log_fav = log(favcount + 1)

. gen log_engagement = log(rt_count + favcount + 1)

. egen userid = group(screen_name)

. bysort date2 userid: gen N_du = _N

. 
. 
. 
. **** Coding TREATMENT (Adamowicz) *******
. 
.         
. gen dayC_ada = date2 - 21562

. gen dayC_sq_ada = dayC_ada*dayC_ada

. gen dayC_cu_ada = dayC_ada*dayC_ada*dayC_ada

. gen post_ada = date2>=21562

. gen dayC_post_ada = dayC_ada*post_ada

. gen dayC_sq_post_ada = dayC_sq_ada*post_ada

. gen dayC_cu_post_ada = dayC_cu_ada*post_ada

. gen dayC_opos_ada = dayC_ada*opposition

. gen dayC_sq_opos_ada = dayC_sq_ada*opposition

. gen dayC_cu_opos_ada = dayC_cu_ada*opposition

. gen dayC_opos_post_ada = dayC_ada*opposition*post_ada

. gen dayC_sq_opos_post_ada = dayC_sq_ada*opposition*post_ada

. gen dayC_cu_opos_post_ada = dayC_cu_ada*opposition*post_ada

. gen post_opo_ada = post_ada*opposition

. gen post_gob_ada = post_ada*government

. 
. gen hour_sq = hour*hour

. gen hour_cu = hour*hour*hour

. 
. bysort userid: gen n_u = _n

. gen ffd_dayssinceE_ada = 99999

. gen ffd_targetdate_ada = 21562

. foreach targetdate in 21562 {
  2.         replace ffd_dayssinceE_ada = (date2-`targetdate') if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  3.         replace ffd_targetdate_ada = `targetdate' if (abs(date2-`targetdate')<=abs(ffd_dayssinceE_ada))
  4. }
(239,481 real changes made)
(0 real changes made)

. gen event_days_ada = date2-21562

. 
. gen timeWindow0_ada = ffd_dayssinceE_ada>-3 & ffd_dayssinceE_ada<2

. gen timeWindow1_ada = ffd_dayssinceE_ada>-6 & ffd_dayssinceE_ada<5

. gen timeWindow2_ada = ffd_dayssinceE_ada>-11 & ffd_dayssinceE_ada<10

. gen timeWindow3_ada = ffd_dayssinceE_ada>-16 & ffd_dayssinceE_ada<15

. gen timeWindow4_ada = ffd_dayssinceE_ada>-21 & ffd_dayssinceE_ada<20

. gen timeWindow5_ada = ffd_dayssinceE_ada>-31 & ffd_dayssinceE_ada<30

. 
. 
. 
. keep if timeWindow3_ada
(229,797 observations deleted)

. 
. gen count = 1

. 
. collapse (sum) count, by(userid) 

. 
. label variable count "Tweets per actor (15-day time window)"

. 
. hist count, bin(20) freq
(bin=20, start=1, width=46.15)

. 
end of do-file

. do "10_twitter_polls.do"

. ********************************************************************************
. *                                                                                                                                   
>                    *
. *                                                       VAP                                                                         
>                        *
. *                                                                                                                                   
>            *
. ********************************************************************************
. 
. * -----> This do-file: Correlation between Twitter engagement and traditional polls
. 
. ********************************************************************************
. 
. set more off

. clear all

. set matsize 3000

. set maxvar 10000


. 
. ************************************
. 
. 
. * Path 
. if "`c(username)'"=="Juan S. Morales" | "`c(username)'"=="jmorales"{
.         global PathData = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "C:/Users/`c(username)'/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         }

. else { 
.         global PathData = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/"
.         global PathFig = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/"
.         global PathTab = "/Users/JNG/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/"
.         } 

. 
. capture program drop save_coef

. program save_coef
  1.         args numCF
  2.         preserve
  3.                 regsave
  4.                 sum coef if var == "_cons"
  5.                 local alpha = r(mean)
  6.                 replace coef = coef + `alpha'
  7.                 drop if var == "_cons"
  8.                 gen monthD = _n
  9.                 keep coef monthD
 10.                 rename coef coef`numCF'
 11.                 sort monthD
 12.                 save "${PathData}coef`numCF'.dta", replace
 13.         restore
 14. end

. 
. import excel "${PathData}cbos_2017_2019.xlsx", sheet("Sheet1") firstrow clear

. gen monthD = _n

. sort monthD

. save "${PathData}cbos_m.dta", replace
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/cbos_m.dta saved

. 
. use "${PathData}tweets_poland.dta", clear

. 
. encode screen_name, gen(user_id)

. gen date = substr(created_at,1,10)
(2 missing values generated)

. gen month = substr(date,6,2)
(2 missing values generated)

. gen year = substr(date,1,4)
(2 missing values generated)

. gen day = substr(date,9,2)
(2 missing values generated)

. gen hour = substr(created_at,12,2)
(2 missing values generated)

. destring(month), replace
month: all characters numeric; replaced as byte
(2 missing values generated)

. destring(year), replace
year: all characters numeric; replaced as int
(2 missing values generated)

. destring(day), replace
day: all characters numeric; replaced as byte
(2 missing values generated)

. destring(hour), replace
hour: all characters numeric; replaced as byte
(2 missing values generated)

. gen time = (year-2012)*12 + month
(2 missing values generated)

. gen timeD = (year-2012)*365 + (month==2)*31 + (month==3)*(31+28) + (month==4)*(31*2 + 28) + (month==5)*(31*2 + 28 +30) + (month==6)*
> (31*3 + 28 +30) + (month==7)*(31*3 + 28 + 30*2) + (month==8)*(31*4 + 28 + 30*2) + (month==9)*(31*5 + 28 + 30*2) + (month==10)*(31*5 
> + 28 + 30*3) + (month==11)*(31*6 + 28 + 30*3) + (month==12)*(31*6 + 28 + 30*4) + day + 1*((month>2 & year==2012)|(year>2012))
(2 missing values generated)

. gen week = round(timeD/7)
(2 missing values generated)

. gen date2 = mdy(month, day, year)
(2 missing values generated)

. gen monthD = (year-2012)*12 + month
(2 missing values generated)

. keep if year>2016
(37,754 observations deleted)

. gen log_engagement = log(favcount + rt_count + 1)
(2 missing values generated)

. gen log_rt = log(rt_count + 1)
(2 missing values generated)

. gen log_fav = log(favcount + 1)
(2 missing values generated)

. 
. areg log_engagement i.monthD if government, abs(user_id)

Linear regression, absorbing indicators         Number of obs     =    127,208
Absorbed variable: user_id                      No. of categories =         59
                                                F(  33, 127116)   =      26.46
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3024
                                                Adj R-squared     =     0.3019
                                                Root MSE          =     1.4584

------------------------------------------------------------------------------
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |
         62  |  -.1547693   .0733479    -2.11   0.035    -.2985299   -.0110087
         63  |  -.1687289   .0670927    -2.51   0.012    -.3002295   -.0372283
         64  |   -.171306   .0614193    -2.79   0.005    -.2916868   -.0509252
         65  |  -.0838635   .0640541    -1.31   0.190    -.2094084    .0416814
         66  |   .0129765   .0619582     0.21   0.834    -.1084604    .1344135
         67  |   .3866476    .059183     6.53   0.000     .2706499    .5026453
         68  |   .2698156   .0592453     4.55   0.000     .1536958    .3859354
         69  |   .2198907   .0568877     3.87   0.000     .1083918    .3313896
         70  |   .1735924   .0565144     3.07   0.002     .0628253    .2843596
         71  |   .2161985   .0562726     3.84   0.000     .1059052    .3264918
         72  |   .4219463   .0565582     7.46   0.000     .3110931    .5327994
         73  |   .3876736   .0557159     6.96   0.000     .2784713    .4968758
         74  |   .3598986   .0572464     6.29   0.000     .2476967    .4721005
         75  |   .1979504   .0578052     3.42   0.001     .0846533    .3112475
         76  |   .1623527    .056902     2.85   0.004     .0508258    .2738796
         77  |   .2002335   .0577482     3.47   0.001      .087048    .3134189
         78  |  -.0104019    .056068    -0.19   0.853    -.1202943    .0994904
         79  |   .2861274   .0562265     5.09   0.000     .1759243    .3963304
         80  |   .3771224   .0571861     6.59   0.000     .2650387    .4892061
         81  |   .2057706   .0536551     3.84   0.000     .1006076    .3109336
         82  |   .2762789   .0519329     5.32   0.000     .1744914    .3780665
         83  |   .4502431   .0545555     8.25   0.000     .3433151     .557171
         84  |   .2681639   .0549044     4.88   0.000     .1605523    .3757755
         85  |   .3106101   .0543014     5.72   0.000     .2041804    .4170398
         86  |    .294946   .0531831     5.55   0.000      .190708    .3991839
         87  |   .0796899   .0525034     1.52   0.129    -.0232158    .1825956
         88  |   .1160512   .0522709     2.22   0.026     .0136011    .2185013
         89  |   .2106973   .0523637     4.02   0.000     .1080654    .3133292
         90  |   .1920503   .0543223     3.54   0.000     .0855796     .298521
         91  |   .0836956   .0533865     1.57   0.117    -.0209411    .1883322
         92  |   .2838248   .0541241     5.24   0.000     .1777425     .389907
         93  |   .2001989   .0524862     3.81   0.000     .0973268    .3030711
         94  |   .2559754   .0531449     4.82   0.000     .1518124    .3601384
             |
       _cons |    2.98454    .049338    60.49   0.000     2.887838    3.081241
------------------------------------------------------------------------------
F test of absorbed indicators: F(58, 127116) = 913.191        Prob > F = 0.000

. save_coef 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     2.98454           .    2.98454    2.98454
(35 real changes made)
(1 observation deleted)
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/coef1.dta saved

. areg log_engagement i.monthD if opposition, abs(user_id)

Linear regression, absorbing indicators         Number of obs     =    164,727
Absorbed variable: user_id                      No. of categories =         51
                                                F(  33, 164643)   =      64.88
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2488
                                                Adj R-squared     =     0.2485
                                                Root MSE          =     1.4201

------------------------------------------------------------------------------
log_engage~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |
         62  |  -.0838698   .0608675    -1.38   0.168    -.2031688    .0354292
         63  |   .0642441   .0558497     1.15   0.250    -.0452201    .1737084
         64  |  -.0262799   .0591649    -0.44   0.657    -.1422419    .0896821
         65  |   .0509879    .057616     0.88   0.376    -.0619382     .163914
         66  |  -.1116504   .0617564    -1.81   0.071    -.2326917    .0093909
         67  |   .2023574   .0487589     4.15   0.000      .106791    .2979237
         68  |   .1643474    .057405     2.86   0.004     .0518348      .27686
         69  |   .0372303   .0528696     0.70   0.481    -.0663931    .1408537
         70  |  -.0339826   .0478059    -0.71   0.477    -.1276812    .0597161
         71  |   .0753884   .0487238     1.55   0.122    -.0201093     .170886
         72  |    .366681   .0523583     7.00   0.000     .2640599    .4693021
         73  |    .326788   .0530698     6.16   0.000     .2227724    .4308035
         74  |   .3084472    .051418     6.00   0.000      .207669    .4092253
         75  |   .2306043   .0491891     4.69   0.000     .1341948    .3270138
         76  |   .2954663   .0482299     6.13   0.000     .2009367     .389996
         77  |   .2699743   .0475429     5.68   0.000     .1767912    .3631574
         78  |   .2469967    .049149     5.03   0.000     .1506657    .3433276
         79  |   .4344096   .0482541     9.00   0.000     .3398327    .5289865
         80  |   .4073506   .0479068     8.50   0.000     .3134543    .5012468
         81  |   .3301468   .0452488     7.30   0.000     .2414602    .4188334
         82  |   .2319304   .0451441     5.14   0.000      .143449    .3204119
         83  |   .5127414    .046369    11.06   0.000     .4218591    .6036237
         84  |   .3724464   .0461204     8.08   0.000     .2820514    .4628414
         85  |   .5889906    .045218    13.03   0.000     .5003642     .677617
         86  |    .667471   .0446479    14.95   0.000     .5799622    .7549799
         87  |   .4574356   .0438775    10.43   0.000     .3714367    .5434345
         88  |   .2778761   .0436858     6.36   0.000     .1922528    .3634993
         89  |   .2309162   .0437934     5.27   0.000      .145082    .3167503
         90  |   .3313864   .0464956     7.13   0.000      .240256    .4225168
         91  |   .3641274   .0437913     8.32   0.000     .2782973    .4499575
         92  |   .7186125   .0451862    15.90   0.000     .6300485    .8071765
         93  |   .5312474   .0440156    12.07   0.000     .4449777    .6175171
         94  |   .4834267   .0444586    10.87   0.000     .3962887    .5705646
             |
       _cons |   3.091341   .0414337    74.61   0.000     3.010132     3.17255
------------------------------------------------------------------------------
F test of absorbed indicators: F(50, 164643) = 866.661        Prob > F = 0.000

. save_coef 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    3.091341           .   3.091341   3.091341
(35 real changes made)
(1 observation deleted)
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/coef2.dta saved

. 
. preserve

. 
. clear

. use "${PathData}cbos_m.dta"

. sort monthD

. merge monthD using "${PathData}coef1.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)

. drop _merge

. sort monthD

. merge monthD using "${PathData}coef2.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)

. drop _merge

. 
. capture drop coef1DT-diffTWEET_z

. tsset monthD
        time variable:  monthD, 1 to 36
                delta:  1 unit

. **detrend**
. gen monthD_sq = monthD*monthD

. forvalues i=1(1)2 {
  2.         reg coef`i' monthD
  3.         predict coef`i'DT, res
  4. }

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      6.41
       Model |  .154038421         1  .154038421   Prob > F        =    0.0165
    Residual |  .769542953        32  .024048217   R-squared       =    0.1668
-------------+----------------------------------   Adj R-squared   =    0.1407
       Total |  .923581374        33  .027987314   Root MSE        =    .15507

------------------------------------------------------------------------------
       coef1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0068608   .0027108     2.53   0.016      .001339    .0123826
       _cons |   3.050109   .0543857    56.08   0.000     2.939329    3.160889
------------------------------------------------------------------------------
(2 missing values generated)

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =     60.55
       Model |  .970060759         1  .970060759   Prob > F        =    0.0000
    Residual |  .512694249        32  .016021695   R-squared       =    0.6542
-------------+----------------------------------   Adj R-squared   =    0.6434
       Total |  1.48275501        33   .04493197   Root MSE        =    .12658

------------------------------------------------------------------------------
       coef2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0172171   .0022127     7.78   0.000       .01271    .0217241
       _cons |   3.064285   .0443912    69.03   0.000     2.973863    3.154707
------------------------------------------------------------------------------
(2 missing values generated)

. reg pis monthD

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =     14.63
       Model |  85.8365164         1  85.8365164   Prob > F        =    0.0006
    Residual |  187.692895        32  5.86540298   R-squared       =    0.3138
-------------+----------------------------------   Adj R-squared   =    0.2924
       Total |  273.529412        33  8.28877005   Root MSE        =    2.4219

------------------------------------------------------------------------------
         pis |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .1619557   .0423359     3.83   0.001     .0757202    .2481912
       _cons |   39.28342   .8493605    46.25   0.000     37.55333    41.01351
------------------------------------------------------------------------------

. predict pisDT, res
(2 missing values generated)

. reg po monthD

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      0.20
       Model |  1.83537051         1  1.83537051   Prob > F        =    0.6560
    Residual |  290.429335        32  9.07591673   R-squared       =    0.0063
-------------+----------------------------------   Adj R-squared   =   -0.0248
       Total |  292.264706        33  8.85650624   Root MSE        =    3.0126

------------------------------------------------------------------------------
          po |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0236822    .052663     0.45   0.656    -.0835888    .1309532
       _cons |   18.73262   1.056546    17.73   0.000     16.58051    20.88473
------------------------------------------------------------------------------

. predict poDT, res
(2 missing values generated)

. 
. gen diffPOLLS = pisDT-poDT
(2 missing values generated)

. egen diffPOLLS_z = std(diffPOLLS)
(2 missing values generated)

. 
. gen diffTWEET = coef1DT - coef2DT
(2 missing values generated)

. egen diffTWEET_z = std(diffTWEET)
(2 missing values generated)

. 
. reg diffPOLLS_z diffTWEET_z

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      4.50
       Model |  4.06793489         1  4.06793489   Prob > F        =    0.0418
    Residual |  28.9320657        32  .904127052   R-squared       =    0.1233
-------------+----------------------------------   Adj R-squared   =    0.0959
       Total |  33.0000005        33  1.00000002   Root MSE        =    .95086

------------------------------------------------------------------------------
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |   .3510993   .1655228     2.12   0.042     .0139405    .6882582
       _cons |  -4.09e-09   .1630705    -0.00   1.000    -.3321636    .3321636
------------------------------------------------------------------------------

. reg diffPOLLS_z l.diffTWEET_z

      Source |       SS           df       MS      Number of obs   =        33
-------------+----------------------------------   F(1, 31)        =      5.48
       Model |  4.95356684         1  4.95356684   Prob > F        =    0.0259
    Residual |  28.0410839        31  .904551094   R-squared       =    0.1501
-------------+----------------------------------   Adj R-squared   =    0.1227
       Total |  32.9946508        32  1.03108284   Root MSE        =    .95108

------------------------------------------------------------------------------
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L1. |   .3877655   .1657016     2.34   0.026     .0498149    .7257162
             |
       _cons |   .0005498   .1655657     0.00   0.997    -.3371237    .3382233
------------------------------------------------------------------------------

. 
. gen date = ym(year,month)
(2 missing values generated)

. label variable diffTWEET_z "Follower engagement"

. label variable diffPOLLS_z "Polls avg."

. label variable date "Date"

. 
. tsset date, month
        time variable:  date, 2017m1 to 2019m10
                delta:  1 month

. set scheme sj

. twoway (scatter diffTWEET_z date, graphregion(color(white)) xsize(6) connect(yes) mcolor(ebblue) lcolor(ebblue)) (scatter diffPOLLS_
> z date, connect(yes) mcolor(cranberry) msymbol(triangle) lcolor(cranberry) )
(note:  named style yes not found in class connectstyle, default attributes used)
(note:  named style yes not found in class connectstyle, default attributes used)

. 
. graph export "${PathFig}Figure1.png", width(3600) as(png) replace
(file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Figures/Figure1.png written in PNG format)

. 
. est clear

. eststo: reg diffPOLLS_z f4.diffTWEET_z, robust

Linear regression                               Number of obs     =         30
                                                F(1, 28)          =       1.57
                                                Prob > F          =     0.2212
                                                R-squared         =     0.0401
                                                Root MSE          =     1.0461

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F4. |   -.230615   .1842979    -1.25   0.221     -.608132    .1469021
             |
       _cons |   .0336124   .1809788     0.19   0.854    -.3371059    .4043307
------------------------------------------------------------------------------
(est1 stored)

. eststo: reg diffPOLLS_z f3.diffTWEET_z, robust

Linear regression                               Number of obs     =         31
                                                F(1, 29)          =       0.40
                                                Prob > F          =     0.5328
                                                R-squared         =     0.0136
                                                Root MSE          =     1.0448

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F3. |  -.1283167   .2032717    -0.63   0.533     -.544054    .2874206
             |
       _cons |  -.0042577   .1864647    -0.02   0.982    -.3856209    .3771055
------------------------------------------------------------------------------
(est2 stored)

. eststo: reg diffPOLLS_z f2.diffTWEET_z, robust

Linear regression                               Number of obs     =         32
                                                F(1, 30)          =       0.88
                                                Prob > F          =     0.3563
                                                R-squared         =     0.0241
                                                Root MSE          =     1.0297

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F2. |   .1578887   .1685254     0.94   0.356     -.186286    .5020634
             |
       _cons |  -.0054138   .1797703    -0.03   0.976    -.3725537    .3617261
------------------------------------------------------------------------------
(est3 stored)

. eststo: reg diffPOLLS_z f1.diffTWEET_z, robust

Linear regression                               Number of obs     =         33
                                                F(1, 31)          =       3.40
                                                Prob > F          =     0.0747
                                                R-squared         =     0.0970
                                                Root MSE          =     .97791

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F1. |   .3124226   .1693898     1.84   0.075    -.0330503    .6578954
             |
       _cons |  -.0179309   .1705769    -0.11   0.917    -.3658247    .3299629
------------------------------------------------------------------------------
(est4 stored)

. eststo: reg diffPOLLS_z diffTWEET_z, robust

Linear regression                               Number of obs     =         34
                                                F(1, 32)          =       3.70
                                                Prob > F          =     0.0632
                                                R-squared         =     0.1233
                                                Root MSE          =     .95086

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |   .3510993   .1824094     1.92   0.063    -.0204564    .7226551
       _cons |  -4.09e-09   .1630705    -0.00   1.000    -.3321636    .3321636
------------------------------------------------------------------------------
(est5 stored)

. eststo: reg diffPOLLS_z l1.diffTWEET_z, robust

Linear regression                               Number of obs     =         33
                                                F(1, 31)          =       4.63
                                                Prob > F          =     0.0393
                                                R-squared         =     0.1501
                                                Root MSE          =     .95108

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L1. |   .3877655   .1802305     2.15   0.039      .020183    .7553481
             |
       _cons |   .0005498   .1658096     0.00   0.997    -.3376211    .3387207
------------------------------------------------------------------------------
(est6 stored)

. eststo: reg diffPOLLS_z l2.diffTWEET_z, robust

Linear regression                               Number of obs     =         32
                                                F(1, 30)          =       3.63
                                                Prob > F          =     0.0665
                                                R-squared         =     0.1216
                                                Root MSE          =     .97893

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L2. |   .3495454   .1835264     1.90   0.066    -.0252655    .7243562
             |
       _cons |  -.0219433   .1727919    -0.13   0.900    -.3748314    .3309448
------------------------------------------------------------------------------
(est7 stored)

. eststo: reg diffPOLLS_z l3.diffTWEET_z, robust

Linear regression                               Number of obs     =         31
                                                F(1, 29)          =       4.96
                                                Prob > F          =     0.0339
                                                R-squared         =     0.1467
                                                Root MSE          =      .9744

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L3. |   .3942256   .1770878     2.23   0.034     .0320404    .7564108
             |
       _cons |   -.019102   .1741211    -0.11   0.913    -.3752196    .3370155
------------------------------------------------------------------------------
(est8 stored)

. eststo: reg diffPOLLS_z l4.diffTWEET_z, robust

Linear regression                               Number of obs     =         30
                                                F(1, 28)          =       9.89
                                                Prob > F          =     0.0039
                                                R-squared         =     0.2011
                                                Root MSE          =     .87093

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L4. |    .420279   .1336566     3.14   0.004     .1464958    .6940622
             |
       _cons |   .0514742   .1572991     0.33   0.746    -.2707383    .3736868
------------------------------------------------------------------------------
(est9 stored)

. 
. label variable diffTWEET_z "Follower engagement"

. label variable diffPOLLS_z "Polls avg."

. 
. esttab est1 est2 est3 est4 est5 est6 est7 est8 est9 using "${PathTab}TableA6.tex", se star(* 0.1 ** 0.05 *** 0.01) varlabels("Follow
> er engagement") stats(N r2, labels("N" "R2")) label title("Correlation between approval polls and Twitter engagement} \scriptsize {"
> ) replace nonotes noconstant nomtitles postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA6.tex)

. 
. restore

.  
. 
. ***Using only likes***
. areg log_fav i.monthD if government, abs(user_id)

Linear regression, absorbing indicators         Number of obs     =    127,208
Absorbed variable: user_id                      No. of categories =         59
                                                F(  33, 127116)   =      31.19
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3311
                                                Adj R-squared     =     0.3306
                                                Root MSE          =     1.6524

------------------------------------------------------------------------------
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |
         62  |  -.0736284   .0831013    -0.89   0.376    -.2365055    .0892486
         63  |  -.1567986   .0760143    -2.06   0.039    -.3057853   -.0078118
         64  |   -.255477   .0695865    -3.67   0.000    -.3918654   -.1190886
         65  |  -.2222323   .0725716    -3.06   0.002    -.3644714   -.0799932
         66  |  -.0877095    .070197    -1.25   0.211    -.2252945    .0498755
         67  |  -.0889998   .0670528    -1.33   0.184    -.2204222    .0424226
         68  |  -.0124167   .0671234    -0.18   0.853    -.1439775     .119144
         69  |  -.0684505   .0644523    -1.06   0.288    -.1947759    .0578749
         70  |  -.0023711   .0640293    -0.04   0.970    -.1278675    .1231252
         71  |   .0235896   .0637554     0.37   0.711    -.1013699    .1485491
         72  |   .2282143    .064079     3.56   0.000     .1026205    .3538081
         73  |   .1671192   .0631247     2.65   0.008     .0433959    .2908425
         74  |   .1127801   .0648587     1.74   0.082    -.0143417     .239902
         75  |   .1643286   .0654918     2.51   0.012     .0359659    .2926913
         76  |   .0413107   .0644685     0.64   0.522    -.0850464    .1676678
         77  |   .1850282   .0654272     2.83   0.005      .056792    .3132644
         78  |   .0342134   .0635236     0.54   0.590    -.0902918    .1587186
         79  |   .0795739   .0637032     1.25   0.212    -.0452833    .2044312
         80  |   .0222646   .0647903     0.34   0.731    -.1047233    .1492526
         81  |  -.1620312   .0607898    -2.67   0.008    -.2811782   -.0428841
         82  |    -.23495   .0588386    -3.99   0.000    -.3502726   -.1196273
         83  |   .0049383     .06181     0.08   0.936    -.1162083    .1260849
         84  |   .0573507   .0622053     0.92   0.357    -.0645705     .179272
         85  |   .1529327    .061522     2.49   0.013     .0323505    .2735148
         86  |   .0957159   .0602551     1.59   0.112     -.022383    .2138149
         87  |   -.002953    .059485    -0.05   0.960    -.1195425    .1136365
         88  |  -.0105051   .0592216    -0.18   0.859    -.1265784    .1055682
         89  |   .1644838   .0593267     2.77   0.006     .0482044    .2807631
         90  |   .1398934   .0615457     2.27   0.023     .0192648    .2605219
         91  |   .2391993   .0604855     3.95   0.000     .1206487    .3577499
         92  |   .3396987   .0613212     5.54   0.000     .2195102    .4598872
         93  |   .3184061   .0594656     5.35   0.000     .2018546    .4349575
         94  |   .1833237   .0602118     3.04   0.002     .0653097    .3013377
             |
       _cons |    1.28998   .0558987    23.08   0.000      1.18042    1.399541
------------------------------------------------------------------------------
F test of absorbed indicators: F(58, 127116) = 1039.997       Prob > F = 0.000

. save_coef 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1     1.28998           .    1.28998    1.28998
(35 real changes made)
(1 observation deleted)
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/coef1.dta saved

. areg log_fav i.monthD if opposition, abs(user_id)

Linear regression, absorbing indicators         Number of obs     =    164,727
Absorbed variable: user_id                      No. of categories =         51
                                                F(  33, 164643)   =      52.23
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2105
                                                Adj R-squared     =     0.2101
                                                Root MSE          =     1.6360

------------------------------------------------------------------------------
     log_fav |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |
         62  |  -.0866739   .0701202    -1.24   0.216     -.224108    .0507602
         63  |  -.1415369   .0643397    -2.20   0.028    -.2676413   -.0154326
         64  |  -.0324869   .0681588    -0.48   0.634    -.1660768    .1011029
         65  |  -.0729273   .0663744    -1.10   0.272    -.2030197    .0571651
         66  |  -.1153379   .0711443    -1.62   0.105    -.2547791    .0241034
         67  |  -.1078126   .0561709    -1.92   0.055    -.2179064    .0022811
         68  |  -.0469759   .0661314    -0.71   0.477    -.1765919    .0826402
         69  |  -.0830971   .0609066    -1.36   0.172    -.2024727    .0362784
         70  |  -.0002642   .0550731    -0.00   0.996    -.1082063    .1076779
         71  |  -.0423959   .0561305    -0.76   0.450    -.1524105    .0676187
         72  |   .0850811   .0603175     1.41   0.158    -.0331398    .2033021
         73  |   .0228399   .0611371     0.37   0.709    -.0969874    .1426673
         74  |   .0109286   .0592342     0.18   0.854    -.1051692    .1270264
         75  |   .1606071   .0566665     2.83   0.005      .049542    .2716722
         76  |   .1833267   .0555616     3.30   0.001     .0744273    .2922262
         77  |   .2235511   .0547701     4.08   0.000     .1162029    .3308993
         78  |   .1484811   .0566203     2.62   0.009     .0375065    .2594557
         79  |    .166877   .0555893     3.00   0.003     .0579231    .2758309
         80  |   .2066288   .0551893     3.74   0.000      .098459    .3147986
         81  |   .1393667   .0521272     2.67   0.008     .0371985    .2415349
         82  |   .1533683   .0520066     2.95   0.003     .0514365    .2553002
         83  |   .3573452   .0534177     6.69   0.000     .2526476    .4620428
         84  |   .2921562   .0531313     5.50   0.000     .1880199    .3962925
         85  |   .3532595   .0520918     6.78   0.000     .2511606    .4553583
         86  |   .3784272    .051435     7.36   0.000     .2776158    .4792386
         87  |   .3741556   .0505474     7.40   0.000     .2750837    .4732275
         88  |   .3779125   .0503266     7.51   0.000     .2792734    .4765517
         89  |    .371738   .0504506     7.37   0.000     .2728558    .4706201
         90  |   .4218708   .0535636     7.88   0.000     .3168873    .5268542
         91  |   .1767353   .0504482     3.50   0.000     .0778578    .2756127
         92  |   .5141774   .0520551     9.88   0.000     .4121504    .6162043
         93  |   .6601205   .0507066    13.02   0.000     .5607366    .7595043
         94  |    .630675   .0512169    12.31   0.000     .5302909    .7310591
             |
       _cons |   .7837669   .0477322    16.42   0.000     .6902128    .8773209
------------------------------------------------------------------------------
F test of absorbed indicators: F(50, 164643) = 846.469        Prob > F = 0.000

. save_coef 2

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        coef |          1    .7837669           .   .7837669   .7837669
(35 real changes made)
(1 observation deleted)
file C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Data/coef2.dta saved

. 
. 
. clear

. use "${PathData}cbos_m.dta"

. sort monthD

. merge monthD using "${PathData}coef1.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)

. drop _merge

. sort monthD

. merge monthD using "${PathData}coef2.dta"
(note: you are using old merge syntax; see [D] merge for new syntax)

. drop _merge

. 
. capture drop coef1DT-diffTWEET_z

. tsset monthD
        time variable:  monthD, 1 to 36
                delta:  1 unit

. **detrend**
. gen monthD_sq = monthD*monthD

. forvalues i=1(1)2 {
  2.         reg coef`i' monthD
  3.         predict coef`i'DT, res
  4. }

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =     18.18
       Model |  .270034837         1  .270034837   Prob > F        =    0.0002
    Residual |  .475307508        32   .01485336   R-squared       =    0.3623
-------------+----------------------------------   Adj R-squared   =    0.3424
       Total |  .745342345        33  .022586132   Root MSE        =    .12187

------------------------------------------------------------------------------
       coef1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0090839   .0021305     4.26   0.000     .0047443    .0134234
       _cons |   1.171479   .0427421    27.41   0.000     1.084416    1.258542
------------------------------------------------------------------------------
(2 missing values generated)

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =    188.29
       Model |  1.35284918         1  1.35284918   Prob > F        =    0.0000
    Residual |  .229912484        32  .007184765   R-squared       =    0.8547
-------------+----------------------------------   Adj R-squared   =    0.8502
       Total |  1.58276166        33  .047962475   Root MSE        =    .08476

------------------------------------------------------------------------------
       coef2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0203322   .0014817    13.72   0.000     .0173141    .0233504
       _cons |   .5950153   .0297269    20.02   0.000     .5344637     .655567
------------------------------------------------------------------------------
(2 missing values generated)

. reg pis monthD

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =     14.63
       Model |  85.8365164         1  85.8365164   Prob > F        =    0.0006
    Residual |  187.692895        32  5.86540298   R-squared       =    0.3138
-------------+----------------------------------   Adj R-squared   =    0.2924
       Total |  273.529412        33  8.28877005   Root MSE        =    2.4219

------------------------------------------------------------------------------
         pis |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .1619557   .0423359     3.83   0.001     .0757202    .2481912
       _cons |   39.28342   .8493605    46.25   0.000     37.55333    41.01351
------------------------------------------------------------------------------

. predict pisDT, res
(2 missing values generated)

. reg po monthD

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      0.20
       Model |  1.83537051         1  1.83537051   Prob > F        =    0.6560
    Residual |  290.429335        32  9.07591673   R-squared       =    0.0063
-------------+----------------------------------   Adj R-squared   =   -0.0248
       Total |  292.264706        33  8.85650624   Root MSE        =    3.0126

------------------------------------------------------------------------------
          po |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      monthD |   .0236822    .052663     0.45   0.656    -.0835888    .1309532
       _cons |   18.73262   1.056546    17.73   0.000     16.58051    20.88473
------------------------------------------------------------------------------

. predict poDT, res
(2 missing values generated)

. 
. gen diffPOLLS = pisDT-poDT
(2 missing values generated)

. egen diffPOLLS_z = std(diffPOLLS)
(2 missing values generated)

. 
. gen diffTWEET = coef1DT - coef2DT
(2 missing values generated)

. egen diffTWEET_z = std(diffTWEET)
(2 missing values generated)

. 
. reg diffPOLLS_z diffTWEET_z

      Source |       SS           df       MS      Number of obs   =        34
-------------+----------------------------------   F(1, 32)        =      8.41
       Model |  6.86540206         1  6.86540206   Prob > F        =    0.0067
    Residual |  26.1345985        32  .816706203   R-squared       =    0.2080
-------------+----------------------------------   Adj R-squared   =    0.1833
       Total |  33.0000005        33  1.00000002   Root MSE        =    .90372

------------------------------------------------------------------------------
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |   .4561167   .1573171     2.90   0.007     .1356723    .7765612
       _cons |  -5.56e-09   .1549864    -0.00   1.000    -.3156969    .3156969
------------------------------------------------------------------------------

. reg diffPOLLS_z l.diffTWEET_z

      Source |       SS           df       MS      Number of obs   =        33
-------------+----------------------------------   F(1, 31)        =     13.84
       Model |  10.1846584         1  10.1846584   Prob > F        =    0.0008
    Residual |  22.8099924        31  .735806207   R-squared       =    0.3087
-------------+----------------------------------   Adj R-squared   =    0.2864
       Total |  32.9946508        32  1.03108284   Root MSE        =    .85779

------------------------------------------------------------------------------
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L1. |   .5651869    .151915     3.72   0.001     .2553543    .8750195
             |
       _cons |  -.0200147   .1493992    -0.13   0.894    -.3247164    .2846871
------------------------------------------------------------------------------

. 
. gen date = ym(year,month)
(2 missing values generated)

. label variable diffTWEET_z "Follower engagement"

. label variable diffPOLLS_z "Polls avg."

. label variable date "Date"

. 
. 
. est clear

. eststo: reg diffPOLLS_z f4.diffTWEET_z, robust

Linear regression                               Number of obs     =         30
                                                F(1, 28)          =       1.62
                                                Prob > F          =     0.2132
                                                R-squared         =     0.0382
                                                Root MSE          =     1.0471

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F4. |   .2064957   .1620945     1.27   0.213    -.1255399    .5385314
             |
       _cons |    -.02722   .1859685    -0.15   0.885    -.4081592    .3537193
------------------------------------------------------------------------------
(est1 stored)

. eststo: reg diffPOLLS_z f3.diffTWEET_z, robust

Linear regression                               Number of obs     =         31
                                                F(1, 29)          =       2.86
                                                Prob > F          =     0.1015
                                                R-squared         =     0.0697
                                                Root MSE          =     1.0147

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F3. |    .262224   .1550331     1.69   0.101    -.0548543    .5793024
             |
       _cons |  -.0293541   .1819502    -0.16   0.873    -.4014841    .3427759
------------------------------------------------------------------------------
(est2 stored)

. eststo: reg diffPOLLS_z f2.diffTWEET_z, robust

Linear regression                               Number of obs     =         32
                                                F(1, 30)          =       2.33
                                                Prob > F          =     0.1370
                                                R-squared         =     0.0574
                                                Root MSE          =      1.012

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F2. |   .2391987   .1565571     1.53   0.137    -.0805334    .5589309
             |
       _cons |  -.0027456   .1784036    -0.02   0.988    -.3670944    .3616032
------------------------------------------------------------------------------
(est3 stored)

. eststo: reg diffPOLLS_z f1.diffTWEET_z, robust

Linear regression                               Number of obs     =         33
                                                F(1, 31)          =       2.58
                                                Prob > F          =     0.1181
                                                R-squared         =     0.0815
                                                Root MSE          =     .98629

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         F1. |   .2856545   .1777059     1.61   0.118     -.076779     .648088
             |
       _cons |  -.0162095   .1713575    -0.09   0.925    -.3656953    .3332763
------------------------------------------------------------------------------
(est4 stored)

. eststo: reg diffPOLLS_z diffTWEET_z, robust

Linear regression                               Number of obs     =         34
                                                F(1, 32)          =       5.79
                                                Prob > F          =     0.0221
                                                R-squared         =     0.2080
                                                Root MSE          =     .90372

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |   .4561167   .1895641     2.41   0.022     .0699872    .8422462
       _cons |  -5.56e-09   .1549864    -0.00   1.000    -.3156969    .3156969
------------------------------------------------------------------------------
(est5 stored)

. eststo: reg diffPOLLS_z l1.diffTWEET_z, robust

Linear regression                               Number of obs     =         33
                                                F(1, 31)          =      18.92
                                                Prob > F          =     0.0001
                                                R-squared         =     0.3087
                                                Root MSE          =     .85779

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L1. |   .5651869   .1299406     4.35   0.000     .3001713    .8302025
             |
       _cons |  -.0200147   .1496437    -0.13   0.894    -.3252149    .2851856
------------------------------------------------------------------------------
(est6 stored)

. eststo: reg diffPOLLS_z l2.diffTWEET_z, robust

Linear regression                               Number of obs     =         32
                                                F(1, 30)          =       6.60
                                                Prob > F          =     0.0154
                                                R-squared         =     0.2286
                                                Root MSE          =     .91737

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L2. |   .4854202   .1889179     2.57   0.015     .0995983    .8712421
             |
       _cons |  -.0385438   .1611198    -0.24   0.813    -.3675943    .2905066
------------------------------------------------------------------------------
(est7 stored)

. eststo: reg diffPOLLS_z l3.diffTWEET_z, robust

Linear regression                               Number of obs     =         31
                                                F(1, 29)          =      12.44
                                                Prob > F          =     0.0014
                                                R-squared         =     0.2599
                                                Root MSE          =     .90747

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L3. |    .519948   .1473919     3.53   0.001     .2184976    .8213984
             |
       _cons |  -.0042394   .1628395    -0.03   0.979    -.3372836    .3288049
------------------------------------------------------------------------------
(est8 stored)

. eststo: reg diffPOLLS_z l4.diffTWEET_z, robust

Linear regression                               Number of obs     =         30
                                                F(1, 28)          =       2.56
                                                Prob > F          =     0.1205
                                                R-squared         =     0.0533
                                                Root MSE          =     .94809

------------------------------------------------------------------------------
             |               Robust
 diffPOLLS_z |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 diffTWEET_z |
         L4. |   .2430334   .1517669     1.60   0.121     -.067847    .5539137
             |
       _cons |   .0992643   .1730857     0.57   0.571    -.2552857    .4538143
------------------------------------------------------------------------------
(est9 stored)

. 
. label variable diffTWEET_z "Follower engagement"

. label variable diffPOLLS_z "Polls avg."

. 
. esttab est1 est2 est3 est4 est5 est6 est7 est8 est9 using "${PathTab}TableA7.tex", se star(* 0.1 ** 0.05 *** 0.01) varlabels("Follow
> er engagement") stats(N r2, labels("N" "R2")) label title("Correlation between approval polls and Twitter likes} \scriptsize {") rep
> lace nonotes noconstant nomtitles postfoot(" ")
(output written to C:/Users/jmorales/Dropbox/adamowicz/4_Draft/CPS_FINAL_submission/Tables/TableA7.tex)

. 
. 
end of do-file

. 
. log close
      name:  <unnamed>
       log:  C:\Users\jmorales\Dropbox\adamowicz\4_Draft\CPS_FINAL_submission\Replication_code\cps_final.log
  log type:  text
 closed on:  15 Nov 2021, 16:22:05
--------------------------------------------------------------------------------------------------------------------------------------
